Hey guys, here’s this week’s edition of the Spatial Edge. We’re like the geospatial newsletter equivalent of Gemini - we used to be cool last year, but we’re slightly less cool with each passing month... In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Population Mapping: Foundation models improve population estimates in data-poor regions.

• Annotation-Free AI: Self-training model eliminates manual geospatial labels.

• Mobility Modelling: Hierarchical approach accelerates large-scale movement simulations.

• Foundation Models: Audit exposes major benchmarking and reproducibility issues.

• Settlement Data: Global 10 metre settlement dynamics dataset released.

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Research you should know about

1. Rethinking population mapping with foundation models

When planning infrastructure or responding to disasters, having accurate, up-to-date population estimates is obviously pretty important. However, as anyone who’s used survey data before, it can be outdated or spatially imprecise, especially in rapidly developing countries. Traditionally, demographers have filled these gaps by estimating populations using a bunch of hand-picked geospatial covariates like satellite imagery of nighttime lights, road networks, and building density. While effective, assembling these datasets is a pain. A new paper tests whether we can skip the manual assembly and instead use the Population Dynamics Foundation Model (PDFM). This model generates embeddings by compressing a bunch of different data, such as search trends, maps, and local activity levels, into a single, reusable mathematical representation of a place (btw, I did a deep dive on embeddings here).

To benchmark this, the researchers compared the predictive power of PDFM embeddings against 23 standard geospatial covariates for estimating subnational populations in Brazil, Nigeria, and the United States. They found that PDFM successfully improved predictive fit, reducing unexplained variance by a median of 20.1% across various machine learning models. However, the performance was pretty uneven. PDFM provided the biggest boost in large, less-developed regions where traditional markers like building density and road networks were weak or absent. In highly developed areas where traditional covariates already captured settlement patterns perfectly, PDFM added very little value.

The study also uncovered a major limitation regarding scale. While PDFM embeddings were highly accurate at the specific spatial resolution they were trained for (e.g., zip codes), their performance broke down predictably when the researchers attempted to aggregate them to larger areas like counties. In contrast, standard geospatial covariates proved much more flexible across different spatial scales. The researchers conclude that while embeddings are useful tools for data-poor environments, they shouldn’t outright replace traditional methods. Instead, the future of population modelling likely lies in hybrid approaches that combine the nuanced, human-activity signals of embeddings with physical geospatial covariates.

2. Eliminating manual annotations for geospatial reasoning

Geospatial reasoning (or I guess any reasoning for that matter) requires models to understand fuzzy, abstract user queries and map them to precise physical locations, like finding the best area to set up emergency shelters after an earthquake. Recent advances in Multimodal Large Language Models (MLLMs) have made it possible for AI to autonomously generate the logical steps to answer these queries. However, the big issue is that teaching these models to actually output the correct geographic coordinates still requires massive amounts of human-annotated bounding boxes. This reliance on labels prevents these models from freely learning and self-evolving using the petabytes of unlabelled satellite imagery currently available.

To address this, a new study introduces RemoteZero, a training framework that requires absolutely zero human bounding-box annotations. The researchers noticed that MLLMs are much better at verifying if a given cropped image matches a text description (the “Eye”) than they are at generating raw coordinates from scratch (the “Hand”). So, RemoteZero uses a “Generate-Crop-Verify” loop. The model basicaly guesses a location, crops that area from the image, and then uses a verifier to check if the crop semantically matches the query. This semantic yes/no score is then fed back into the model as a reward using Group Relative Policy Optimization (GRPO), teaching it to find the right locations without ever seeing a human label.

Interestingly, RemoteZero enables the model to teach itself. Instead of relying on a larger, external model to act as the verifier, RemoteZero can use its own frozen policy from a previous training round to verify its current guesses, creating a continuous loop of self-evolution. When tested against fully supervised baselines that rely on thousands of manual annotations, the self-evolving RemoteZero model actually outperformed them, scoring 71.29% accuracy on the EarthReason benchmark. This proves that intrinsic semantic verification is a viable path forward, potentially unlocking the ability to train powerful geospatial AI using entirely unlabelled satellite data.

3. A smarter way to model mobility

Simulating how users move around in large-scale networks, like fog computing environments, can be pretty difficult without good real-world data. When researchers try to build mobility models from scratch, they often run into massive computational bottlenecks because tracking every single access point in a large area is overwhelming. A new study tackles this issue by proposing a hierarchical method that groups Wi-Fi access points based on their geospatial features. Rather than treating every access point as an isolated spot, they’ve organised them into nested levels, such as moving from an entire building down to a specific wing, then a floor, and finally the individual access point. This top-down approach means it’s a lot easier to track user trajectories and simulate realistic movement without getting bogged down by the sheer volume of network data.

The team analysed wireless access logs from the University of the Balearic Islands campus, using tracking data from the Aruba Location Engine. By applying clustering algorithms to profile user behaviour at different granularities, they generated transition matrices that accurately mapped out where people were likely to go next. The results were pretty impressive. Compared to flat, non-hierarchical models, they layered approach massively reduced the computational complexity. It actually slashed the execution time needed for clustering from several hours down to just a few minutes, while drastically cutting the optimal number of clusters required.

4. The messy state of geospatial foundation models

I know, I know. Another Spatial Edge newsletter, another foundation model paper… But as long time readers may know, I’m still a bit skeptical about how useful they are (more info on this here). But a massive audit of 152 papers reveals a pretty big problem, which is that nobody actually knows what the state of the art is. The researchers found that the current literature is a bit of a mess when it comes to standardising evaluations and testing protocols. E.g. they documented 46 cases where different papers reported wildly different scores (sometimes off by tens of points) for the exact same model and benchmark. Without shared controls or a common evaluation harness, comparing these models is nearly impossible.

The issues go deeper than just mixed-up scorecards though. The audit discovered that 39% of papers do not even release their model weights 🤯. On top of that, researchers are frequently tweaking both the model architecture and the pretraining data at the same time without isolating the variables. This means when a new model claims a massive leap in accuracy, you can’t really tell if the improvement came from a new design or just a massive new dataset. It creates a confusing environment where weak models can hide behind massive datasets, and actual architectural progress gets lost in the noise.

Geospatial Datasets

1. Global settlement exposure dataset

The World Settlement Footprint dataset provides global 10-m resolution settlement dynamics published as a cloud-native GeoZarr layer. Access the data here and the platform here.

2. Urban rooftop reflectivity dataset

The Heat Resilience dataset provides high-resolution, building-level rooftop reflectivity data across more than 50 global cities to support urban heat mitigation. Produced by fusing 10-metre Sentinel-2 satellite observations with 30-centimetre commercial satellite imagery, the model reconstructs granular albedo profiles for individual structures. Access the data and documentation here.

3. Small-area global election returns dataset

The Small-Area Global Elections (SAGE) dataset compiles lower-house parliamentary and presidential election returns for 110 countries at the smallest unit published by local electoral authorities. The repository contains roughly ten billion votes across more than eight million geocoded units, with candidate and political party identifiers harmonised across multiple languages. Access the data here and the code here.

Other useful bits

• I really loved this article from Bruno Sanchez. He argues that Europe consistently loses its home-grown space innovations to foreign buyers because public funding focuses on de-risking supply rather than de-risking market demand. To bridge the downstream investment gap, ESA must act as a committed anchor customer through fixed-price contracts that de-risk future commercial demand and keep scaling companies rooted in Europe.

• SkyFi has launched SkyFi for ArcGIS, bringing on-demand satellite imagery directly into Esri workflows via a native Experience Builder widget and live WMTS streaming. The integration allows users to search archives, task satellites, and stream map layers across ArcGIS platforms without switching applications or manually downloading files.

• The 2026–2027 World Bank Country Income Classifications reveal that six countries advanced to higher income categories through different economic mechanisms, including export-led expansion, broad-based domestic growth, and recovery from financial crisis. Non-growth factors such as statistical revisions to national accounts and downward population census adjustments also played decisive roles in pushing certain economies past the per-capita income thresholds.

• UNESCO has launched an open-learning course on Flood Hazard and Exposure Mapping to train participants in flood modeling, risk assessment, and exposure analysis. Featuring hands-on exercises and real-world case studies from Mozambique, the program equips learners to evaluate urban, coastal, and riverine flood impacts for informed decision-making.

Jobs

• Arsenal FC is looking for a Research Engineer under their Data Analytics team.

• The World Bank is looking for a Data Scientist (Consultant) based in Washington.

• WSC is looking for a remote Data Management Specialist based in Canada.

• UNDP is looking for a Data Assistant based in Rio de Janeiro.

• Esri Netherlands is looking for an Analytics Consultant based in Rotterdam.

Just for Fun

A new Smithsonian exhibition marks the US 250th anniversary by mapping the country through its natural contours, such as watersheds, biomes, and fossil beds.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that’s as anticipated as The Odyssey. The aim, as usual, is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Fine-Grained Damage Assessment: Drone AI improves post-disaster mapping.

• Remote Sensing Agents: RS-Claw streamlines AI tool selection.

• Cross-Border Generalisation: Foundation models struggle across countries.

• Night-Time Dataset: Sentinel-2 releases high-res nighttime imagery.

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Research you should know about

1. Fine-grained damage assessment from drone imagery

Rapid and accurate damage assessment is pretty important after natural disasters, but manual field surveys are often too slow or can be too dangerous to conduct. So automated semantic segmentation models offer a faster alternative, yet they frequently struggle to provide the fine-grained detail required for effective emergency response. When researchers feed high-resolution drone imagery into these models, they typically downsample the images to fit into memory, which actually has the effect of destroying subtle textures that distinguish minor roof damage from major structural collapse. Furthermore, the datasets are heavily imbalanced, with vast amounts of background scenery and very few examples of critically damaged buildings, which often causes standard models to ignore these rare but important hazards.

DA-SegFormer, an adaptation of the SegFormer architecture, is specifically tuned to extract disaster data from high-resolution drone imagery. Instead of aggressive downsampling, the authors use a resolution-preserving protocol where the model processes images in sliding windows, keeping native details intact so it can accurately identify fine-grained damage. To combat the severe class imbalance, they introduced a sampling strategy that forces the model to focus on rare damage cases. They also combined Online Hard Example Mining with Dice Loss, which effectively forces the network to concentrate on the visually subtle decision boundaries between different levels of damage rather than just the easy-to-identify background.

The model shows real promise for emergency responders, as it outperformed the baseline on the RescueNet benchmark with a 74.61% mean intersection over union. Interestingly, it delivered double-digit gains for critical damage classes, identifying minor and major damage far more accurately than standard models. These granular improvements allow for much more reliable automated assessments that help responders allocate help where it is needed most.

2. OpenClaw for Remote Sensing

If you go on X these days, you’re going to be drowning in all the chatter about AI agents. And it’s no surprise that the remote sensing community is also looking at agents. One of the ongoing problems though, is how these agents select their tools. Most current setups just dump the entire list of available tools into the system prompt. Since the remote sensing tool ecosystem is massive, this leads to context bloat and can oftentimes lead to the agent choosing the wrong tool. It creates a major trade-off where agents either struggle to fit everything in their memory or end up omitting critical tools entirely, as they are essentially passive recipients of a flat, static list.

To solve this, researchers introduce RS-Claw. Rather than being passive recipients of a static tool list, RS-Claw agents navigate a hierarchical skill tree. The model essentially discovers and loads tools on demand, moving from a high-level skill summary down to detailed execution documents only when necessary. This progressive disclosure approach keeps the reasoning space clean and free of semantic noise. When tested on the Earth-Bench benchmark, RS-Claw outperformed existing passive baselines across various reasoning tasks, successfully filtering out irrelevant information while slashing input token requirements by up to 86%.

The results show that treating tool acquisition as a dynamic process rather than a static setup significantly improves agent reliability in complex, long-horizon tasks. This is a pretty big shift for geospatial AI, as it allows for much larger, more diverse tool libraries to be used effectively without overwhelming the model.

3. Do foundation models really generalise across borders?

Researchers often hope that foundation models trained on massive satellite archives will provide universal visual features that work anywhere in the world. A new study puts this to the test by using a Leave-One-Country-Out evaluation protocol to see if these models can accurately predict smallholder maize yields across different countries in sub-Saharan Africa. By training models in four countries and testing them on a fifth, the author aimed to find out if geospatial foundation models, like IBM and NASA’s Prithvi-EO, could overcome the usual struggles of cross-country yield transfer.

The results were pretty interesting. While models performed reasonably well when tested on data from the same country, they completely collapsed when asked to predict yields in a new, unseen country, often performing worse than a naive baseline that simply predicts the training set average. Surprisingly, the frozen embeddings from the Prithvi foundation model didn’t provide any meaningful advantage over a standard set of 10-band spectral medians. The author argues that this isn’t necessarily a failure of the foundation models themselves, but rather a reflection of fundamental data distribution shifts in yield across different national contexts that satellite imagery alone cannot easily bridge.

This study serves as a necessary reality check for the field, demonstrating that geospatial pre-training alone can’t close the generalisation gap when yield statistics vary wildly between regions. It establishes a clear, reproducible negative benchmark that should push future research toward more robust domain adaptation and meta-learning techniques.

Geospatial Datasets

1. Sentinel-2 night-time campaign dataset

The Sentinel-2 night-time campaign dataset provides a collection of 56 night-time satellite acquisitions captured during a dedicated observation campaign in December 2025. You can access the data here.

2. Southeast Asian high-resolution climate dataset

The MSEA-Daily dataset provides a high-resolution, gridded daily climate inventory at a 4-km 1/24° spatial scale for mainland Southeast Asia, covering Myanmar, Thailand, Laos, Cambodia, and Vietnam from 1981 to 2014. You can access the data here and the code here.

3. Climate Data Store expanded dataset

The Copernicus Climate Change Service (C3S) has expanded its Climate Data Store (CDS) with a bunch of new open-access climate records and sectoral indicators spanning the atmosphere, land, ocean, cryosphere, and water cycle. You can access CDS here.

4. Cross-platform flood detection dataset

The Sen2GF3Floods dataset provides a multi-source flood mapping resource consisting of 21,483 standardised 10-m resolution image patches (256 × 256 pixels) covering nine major global flood events. You can access the data here and the code here.

Other useful bits

• Google Research found that in 10 major US cities, systematically rerouting under 2% of navigation app users away from bottlenecks can measurably improve traffic speeds and reduce carbon emissions for all road users. By dispersing traffic across underutilised peripheral roads, this is a scalable approach to enhancing network-wide efficiency and urban sustainability.

• Planet Labs is advancing its "Planetary Intelligence" initiative by embedding AI across its satellite hardware, data processing, and user applications to deliver near-real-time Earth observation insights. By integrating edge compute into satellites, sharpening image resolution via AI, and testing a natural-language mapping agent, the company aims to make complex global monitoring accessible to non-technical users.

• European Southern Observatory says that surpassing 100,000 satellites in orbit could render ground-based telescopes obsolete by causing irreversible light pollution and image corruption. The research notes that proposed megaconstellations, including thousands of highly reflective orbital mirrors, threaten to brighten the night sky by up to 300% and permanently disrupt global astronomical observations.

Jobs

• Esri Netherlands is looking for an Analytics Consultant based in Rotterdam.

• UNOPS is looking for an ICT Officer based in Valencia.

• UNDP is looking for a Spatial Planning and GIS Expert based in Malaysia.

• ESA is accepting applications for their mid-year internship opportunities under:

  • Spaceship EAC Team

  • Spaceship ECSAT Team

  • LUNA Analog Facility Team

  • Vulcan Team

Just for Fun

ESA has marked the 1000th image of its "Earth from Space" series with a Copernicus Sentinel-3 satellite capture of Florida, Cuba, and the Bahamas, returning to the exact region featured in the programme's inaugural 2004 edition.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — the GPT-5.6 Sol of geospatial news. We’re not as impressive as some other news outlets, but we’ll get you where you need to go… In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• US Census: Harmonising 30 years of housing data.

• Green Growth: Satellites track cleaner economic growth.

• Africa’s Power: Mapping future electricity expansion.

• Methane Trends: Wetlands mask emission reductions.

• Earthquake Damage: Satellite dataset for building damage.

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Research you should know about

1. Fixing the fragmented history of US census data

For researchers trying to track long-term socioeconomic trends in the United States, the decennial census can be a pain... Every ten years, the census redrawing process updates the boundaries of ‘block groups’ to reflect population shifts and urban development. While this keeps current data accurate, it basically fractures the historical record, creating geographic gaps and mismatches that make it almost impossible to directly compare modern housing statistics against those from past decades. A new study published in Scientific Data tries to address this exact problem, presenting a geospatial framework that successfully reconstructs lost or misaligned historical housing data and harmonises it to modern census boundaries.

Using California as a primary test case, the researchers pulled block-group level housing data from the 1990, 2000, 2010, and 2020 censuses. To fix the thousands of missing or incompatible data points across the older datasets, they employed Ordinary Kriging (an interpolation method that essentially estimates missing values by analysing the spatial patterns of the surrounding data). Once these historical gaps were filled, the team used zonal statistics to map the repaired historical data onto the most recent 2020 census block group boundaries. The end result is a single, seamless dataset that allows for direct, apples-to-apples comparisons of California’s median housing values across a 30-year span.

This framework resolves a major blind spot in longitudinal research, providing urban planners and policymakers with a reliable, continuous view of historical housing trends without the noise of shifting administrative borders.

2. Tracking green growth from space

Cities generate over 60% of the world’s GDP and roughly 70% of its greenhouse gas emissions, making them the ultimate battleground for the concept of “green growth”. The problem is that measuring this at a local level has always been a nightmare. Relying on self-reported city emission inventories is messy, and global models are too broad to capture what’s actually happening block by block. A new paper from Nature Cities tackles this by turning to space, using satellites to directly monitor nitrogen dioxide (NO₂) pollution across 5,435 cities worldwide between 2019 and 2024.

The researchers combined this high-resolution satellite pollution data with fine-grained local GDP estimates, and fed both into an advanced statistical model that stripped away the “noise” of local weather patterns. This allowed them to map the “decoupling” of economic growth from fossil fuels. The results show a massive shift: roughly 80% of the 2,475 cities with significant trends successfully achieved relative decoupling, acting as “cleaner + richer” hubs. This trend was heavily concentrated in China, Europe, and North America, where cities managed to grow their economies while driving down their NO₂ footprints, likely through a combination of strict regulations, electrification, and a shift away from heavy industry.

However, the data also highlighted a concerning counter-trend. Around 16% of cities (mostly located in India, Iran, and Russia) fell into the “dirtier + richer” category, locking themselves into a trajectory where economic expansion remains fundamentally tied to fossil-fuel-burning infrastructure and unregulated industrial growth. This new satellite-based framework provides a highly objective, globally consistent tool for policymakers to monitor the real-time effectiveness of green interventions.

3. A look at Africa's power future

Africa’s electricity sector is growing rapidly to meet massive demand, but keeping track of exactly where and how this power is being generated has been surprisingly difficult. A new study from Nature Communications changes that by introducing a detailed spatial database of African power plants. The researchers spent huge amounts of time data mining open access sources to map out 3,139 operational, under construction, and planned power facilities across the continent. By tracking this expansion up to 2030, they have given us a pretty incredible look at how the continent’s energy landscape is shifting over the next few years.

The numbers point to a massive boom in energy generation. The researchers forecast a 57% increase in electricity production between 2023 and 2030. While fossil fuels are still set to dominate, renewables are picking up serious momentum and are expected to grow from 19% to 34% of the total energy mix. However, this growth comes with some pretty big trade-offs. The surge in new hydropower dams will require a massive amount of water, which is going to put intense pressure on already stressed river basins like the Zambezi, Nile, and Congo. At the same time, the ongoing reliance on coal and natural gas means carbon emissions will continue to climb, even though the continent’s current build rate is actually falling slightly short of its international climate commitments.

4. Natural wetlands drive global methane rebound

Methane is a greenhouse gas that has been rapidly accumulating in our atmosphere, with growth rates hitting record highs in 2020 and 2021 before slowing down in 2023. While international agreements, like the Global Methane Pledge, aim to slash anthropogenic methane emissions by 30% by 2030, one of the big challenges is untangling human-made emissions from natural ones. A new paper from Nature Communications addresses this by using a blend of satellite data to track global methane budgets from 2019 to 2024, revealing that massive fluctuations in the Earth’s natural wetlands and atmospheric chemistry are actively masking human mitigation efforts.

The team used an advanced atmospheric inversion system called GONGGA, feeding it high-resolution satellite imagery from TROPOMI and GOSAT. They found that during the 2020–2022 La Niña period, the “methane sink” (i.e. the chemical process where hydroxyl radicals naturally scrub methane from the atmosphere) weakened significantly, driving up global methane levels. However, as the El Niño weather pattern took hold in 2023, the sink recovered and massive droughts across South America suppressed wetland emissions, bringing the atmospheric methane growth rate back down to 2019 levels. But in 2024, as La Niña returned and the droughts broke, wetland emissions rebounded violently. This massive natural release of methane effectively offset the recovering atmospheric sink, resulting in a near-baseline growth rate that hid the underlying surge in emissions.

The study highlights a pretty big problem for global climate policy. The natural variability of wetland emissions and the atmospheric sink combined to create an annual swing of 12.6 million tonnes of methane. This is essentially a figure large enough to entirely erase a full year of the emission cuts promised under the Global Methane Pledge. The researchers stress that as climate change makes weather patterns like La Niña more frequent, these natural methane releases will likely intensify. This means that tracking progress on global emission targets will require sophisticated satellite monitoring systems that can explicitly separate natural wetland surges from actual human emission reductions.

Geospatial Datasets

1. Paleoclimate reconstruction dataset

The DPastCliM-NA dataset provides a bias-corrected, high-resolution monthly reconstruction of temperature and precipitation for North America over the past 2,000 years, featuring explicit uncertainty estimates. You can access the data here and the code here.

2. Earthquake building damage dataset

The Turkey Earthquake change detection dataset (TUE-CD) comprises 1,656 pairs of bi-temporal satellite images captured by the WorldView-2 satellite to assess building damage after the February 2023 earthquake. You can access TUE-CD here and MSI-Net here.

3. Vectorised building footprint dataset

The PCFootprint dataset provides the first large-scale public baseline for extracting vectorised building footprints from airborne laser scanning point clouds. You can access the data here.

4. Disaster risk knowledge graph dataset

An AI-powered data pipeline transforms millions of global news articles from 2014 to 2024 into structured disaster risk knowledge graphs. You can access the data here, the code here, and the interactive dashboard here.

Other useful bits

• Google’s Gemini 3.1 model allows organisations to automate the transformation of unstructured street-level imagery into structured spatial databases through multimodal visual reasoning. By using bounding box detection and integrated code execution, this approach removes the need for training bespoke computer vision models and streamlines complex infrastructure analysis.

• Fine-scale vegetation mapping in West Kalimantan reveals that high-res data is critical for identifying fragmented forests and community agroforestry systems often obscured by conventional land-use planning. This mapping project provides local communities and policymakers with an accurate evidence base to protect biodiversity and prevent illegal plantation expansion into protected peatlands.

• Innovators are tackling the energy supply bottleneck created by AI power demands by developing space-based solar technologies that beam continuous energy directly to terrestrial grids. Rather than necessitating new infrastructure, this method uses near-infrared light to augment the output of existing solar farms, significantly increasing their operational value and reliability.

Jobs

• Vantor is looking for a (1) Geospatial Data Scientist and a (2) Senior Geospatial Data Engineer based in McLean.

• The University of Illinois is looking for a Research Specialist (GIS Analyst) under their Department of Paediatrics based in Chicago.

• Planet is looking for a Pre-Sales Solutions Architect based in Latin America (preferably Colombia).

• ESA is accepting applications for their mid-year internship opportunities under:

  • Spaceship EAC Team

  • Spaceship ECSAT Team

  • LUNA Analog Facility Team

  • Vulcan Team

Just for Fun

South Korean map service Kakao Map has gone viral for providing unexpectedly detailed geographical data on North Korea, including labels for mountains, universities, and railway stops that remain largely obscured on competing platforms.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — your weekly map to the latest geospatial news. The aim, as always, is to make you a better geospatial data scientist in less than five minutes a week.

First up, I’ll be heading to the AI For Good Summit in Geneva next Tuesday, so hit me up if you’ll be around. I’m always up for a coffee and a geospatial chat.

In today’s newsletter:

• Rural Markets: Satellites track weekly market activity.

• Urban Hazards: Geospatial models flag danger zones.

• Groundwater Loss: Urban sprawl threatens aquifers.

• Urban Weather: Atlanta gets high-res meteorology data.

• AI Economy: Stanford tracks AI’s economic impacts.

• 5G Networks: Glasgow releases urban performance data.

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Research you should know about

1. A new way to measure rural economic activity

In many rural areas, weekly markets are where a lot of the local economy actually happens. Farmers sell crops, traders move goods around, and households buy what they need. But these markets are often informal, scattered across remote areas, and poorly recorded in official data. A new Nature Communications study shows that satellites can help fill this gap. The basic idea is that outdoor markets look different on market days than on other days, because of stalls, crowds, vehicles, and temporary structures. A single satellite image might barely show this, but a long time series of images can reveal a repeated weekly pattern.

The authors use PlanetScope imagery to compare each image with nearby non-market-day reference images, then look for places where brightness and colour change on a regular day-of-week cycle. They validate the approach using known marketplace data from Kenya, Malawi, and Mozambique, where the method achieves high precision, generally above 90% across a broad range of signal strengths. They then apply it across Ethiopia, excluding Addis Ababa, and map 1,776 periodic markets between 2017 and 2024. The resulting map is pretty revealing: around 70% of Ethiopia’s population lives within 10 km of one of these detected markets, and 63% of the markets sit in places with no measurable night-time light emissions. So this picks up a kind of rural economic activity that nightlights would mostly miss.

The activity data is also interesting. Market activity rises and falls with the agricultural calendar, increasing around harvest periods and responding to rainfall shocks. A one standard deviation increase in growing-season rainfall is associated with a roughly 4.2 point increase in harvest-season market activity. The authors also use the data to track disruption during Ethiopia’s recent conflict, finding that periods with more conflict events generally line up with lower market activity. The method has limits, especially where markets are covered, hidden by trees, very cloudy, or not operating around the satellite overpass time. But as a way to monitor rural economic life at high frequency, especially where surveys are sparse or unsafe, this is a pretty neat addition to the geospatial toolkit

2. Spotting urban danger zones with geospatial models

A new Scientific Reports paper looks at a pretty useful problem in urban remote sensing: how to monitor several hazards at the same time. Most satellite-based hazard studies focus on one thing, like floods, wildfire risk, or land movement. This paper tries to bring these together by combining AI with several sources of satellite imagery to map four hazards across two areas in Puglia, southern Italy: floods, land displacement, sediment connectivity, and urban wildfires.

The authors used a mix of Sentinel-1, Sentinel-2, COSMO-SkyMed, and SAOCOM data, alongside environmental and human-made features like elevation, slope, rainfall, soil stability, road distance, land cover, and population density. They trained separate random forest models for each hazard on 100 x 100 m grid cells, then used SHAP analysis to see which factors were actually driving the predictions. This is the interesting bit: the model shows why a place is being flagged. For example, flood risk was strongly linked to drainage position, elevation, soil thickness, and satellite-derived flood probability, while wildfire risk was mainly explained by fuel availability, land cover, and fire climate conditions.

The results were fairly strong, especially for land displacement, sediment connectivity, and wildfires. The multi-source approach generally beat single-hazard models, with big gains for land displacement in both study areas. The SHAP analysis also picked up some useful local patterns. In Gargano, steep slopes and post-wildfire conditions helped explain land instability and sediment movement. In Bari, land displacement was more closely linked to roads, pointing to the role of construction, altered drainage, and other human pressures. The broader takeaway is that multi-hazard mapping works best when it treats hazards as connected systems, rather than separate layers on a map.

3. The toll of urban sprawl on groundwater

As cities continue to expand rapidly around the globe, they transform the natural landscape by replacing soil and vegetation with concrete and asphalt. This unchecked urban sprawl poses a bit of a threat to underground aquifers, which rely on permeable land to soak up rainfall and replenish their stores. However, the exact impact of this urban growth on groundwater recharge and quality has been difficult to map and quantify on a city-wide scale. A new paper from Scientific Reports addresses this gap by focusing on Nnewi, a rapidly expanding industrial city in southeastern Nigeria, using a combination of remote sensing satellites and GIS to analyse changes over the past decade.

The researchers used Landsat imagery from 2013 and 2024 to map the changing landscape, feeding the data into a GIS model to calculate groundwater recharge potential. The results were pretty big. Over the eleven-year period, dense vegetation plummeted by 71%, while impervious, built-up surfaces expanded significantly to cover over 11% of the total area. Crucially, the model revealed that areas capable of recharging the aquifer now make up just 28% of the city. Furthermore, the expansion of concrete and loss of vegetation not only starved the aquifers of water but directly caused water quality to plummet, with industrial and high-density residential areas showing dangerous spikes in nitrates, heavy metals, and dissolved solids.

The study proves that as a city paves over its natural landscape, it effectively suffocates its own water supply while simultaneously trapping and funnelling pollutants directly into the remaining water table. These findings serve as a bit of a warning for rapidly urbanising cities, especially in Sub-Saharan Africa, where groundwater is often the primary source of drinking water. The authors stress that city planners must urgently incorporate “green infrastructure,” such as permeable pavements and urban parks, to restore the natural water cycle before the damage to local aquifers becomes irreversible.

Geospatial Datasets

1. High-res urban meteorology dataset

The HUMID-Atlanta dataset provides a 1-square-kilometre resolution hourly meteorological record covering the Atlanta metropolitan area from 2010 to 2023. Access the data here and the code here.

2. AI economic indicators

Stanford Digital Economy Lab’s AI Economic Indicators provide a comprehensive framework to monitor the macroeconomic and labour market impacts of AI across the US. It combines three measurement tools: the Canaries Dashboard for payroll employment tracking, the Takeoff Tracker for explosive economic growth indicators, and the Adoption Monitor for firm-level survey results. Access the indicators here.

3. Land-atmosphere coupling metrics dataset

The Global Observationally-Based Land-Atmosphere Coupling Metrics (GOLAM) dataset provides global fields of land-atmosphere interaction parameters at spatial resolutions of 0.1° and 0.25°. Access the data here and the code here.

4. Urban 5G network performance dataset

The Glasgow Public and Private 5G Performance Dataset provides 720 empirical outdoor network measurements collected across 15 Glasgow neighbourhoods over a three-day period in April 2025. Access the data and code here.

Other useful bits

• A comparative satellite analysis has highlighted 10 global water bodies suffering from severe "continental drying" caused by climate change and unsustainable land use. Long-term imagery reveals that critical freshwater sources, including Argentina's Parana River, Iran's Lake Urmia, and the South Aral Sea, have experienced drastic volume reductions that threaten local ecosystems and regional water security.

• Google Research has developed a high-res deep learning framework that converts pixel-based satellite imagery of agricultural landscapes into a vectorised dataset of fine-scale ecological features. This automated pipeline uses Google Earth AI and Google Earth Engine to programmatically classify and map millions of small-scale vegetation structures across England, providing a precise inventory to support nature restoration without compromising food security.

• The open-access Harvest2Market platform, developed by NASA Harvest, centralises satellite imagery, market indicators, shipping data, and global crop reports to bridge information silos in agricultural monitoring. This web-based system enables analysts to evaluate food supply chain risks by mapping how regional events, such as severe weather or trade disruptions, impact interconnected global markets.

• A satellite has successfully utilised a vision-language model in orbit to identify specific environmental features and infrastructure from natural language queries without ground-based human analysis. The demonstration, conducted on Loft Orbital's YAM-9 spacecraft using a streamlined version of Google DeepMind’s Gemma 3 model, proves the viability of on-orbit data triage and paves the way for autonomous, real-time monitoring constellations.

Jobs

• EIB is looking for (1) a Data Warehouse Officer and (2) an Associate DevSecOps Engineer, both based in Luxembourg.

• UNOOSA Germany is looking for a GIS / Remote Sensing Expert based in Bonn.

• Médecins Sans Frontières UK is looking for (1) a Missing Maps Coordinator and (2) a GIS Specialist (Maternity Cover) based in London

• IOM is looking for a Data and Research Intern based in Vienna

Just for Fun

Back-on-Track.eu has launched an interactive 2026 European night train map tracking 205 regular sleeper lines, highlighting five new routes alongside ten closures.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — we’re sort of like the R of the geospatial world. Free, intuitive, but for some reason people still keep going back to Stat…In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Earthquake Risk: Bayesian models predict building-level damage.

• Disaster Tracking: High-frequency satellites monitor fires and storms.

• Wind Siting: AI identifies optimal wind farm locations.

• Smart Farming: Decentralised AI improves pest management.

• Field Mapping: Three billion agricultural fields mapped globally.

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Research you should know about

1. Predicting earthquake damage with physics and statistics

Earthquakes pose a massive threat to dense urban areas, but predicting exactly how a city will hold up during a major tremor can be pretty complex. Standard risk assessments often rely on simplified charts or historical data, which can miss the nuanced structural differences between individual buildings. A new paper from Scientific Reports presents a large-scale seismic risk framework that combines rigorous physics-based structural modelling with the predictive power of Bayesian networks. By focusing on District 2 of Tehran as a case study, the team managed to evaluate thousands of buildings to map out precise zones of vulnerability.

The researchers categorised the district’s buildings based on their age, number of storeys, and structural materials (such as steel or reinforced concrete). They then ran intensive simulations to see how each building type would react to varying earthquake intensities. Because it’s obviously impossible to perfectly predict every variable in a sprawling city, the team fed their simulation results into a Bayesian probabilistic network, which is a statistical model that excels at handling uncertainty. The results revealed that newer buildings generally fare better, but specific vulnerabilities stood out. For instance, reinforced concrete structures were more prone to damage from horizontal shifting (drift), while steel buildings suffered more from raw shaking acceleration. Alarmingly, the model pinpointed two specific zones in the district facing up to a 59% chance of complete destruction in a severe earthquake.

This granular, data-driven approach gives city planners and emergency responders a bit more of a sharper tool for identifying weak points in urban infrastructure before disaster strikes.

2. Tracking fast-moving disasters from space

As climate-related disasters like wildfires and flash floods become more frequent and severe, the need for real-time monitoring from space has never been greater. Earth Observation (EO) models powered by artificial intelligence have made massive leaps recently, with developers building geospatial foundation models (more on that here and here), that are trained on vast amounts of unlabelled satellite imagery. However, nearly all of these existing models are trained on imagery from polar-orbiting satellites, which provide incredibly detailed, high-resolution pictures but only pass over the same spot on Earth every few days. This makes them entirely unsuitable for tracking fast-moving, dynamic events like a rapidly spreading forest fire or a developing storm system.

A new study introduces HighFM, a new foundation model explicitly designed for high-frequency environmental monitoring. Instead of using highly detailed but infrequent images, the team trained their model on more than 2 terabytes of imagery from the Meteosat Second Generation platform. These geostationary satellites provide lower-resolution images but update every 15 minutes, offering a near-constant view of the Earth. To handle this unique data, the researchers adapted a masked autoencoder architecture to retain fine-grained, minute-by-minute temporal information that other models typically discard.

To test HighFM, the researchers asked the model to identify active fires and map cloud cover. When benchmarked against traditional AI models and even other state-of-the-art EO foundation models, HighFM consistently proved superior. The model’s ability to understand the rapid, short-term variability between images allowed it to detect fires and clouds with greater accuracy and fewer false alarms. The researchers noted that while HighFM was specifically tested on fire and cloud detection, the architecture could easily be adapted for tracking other rapid phenomena, like storm development or solar energy forecasting, offering a powerful new tool for civil protection and disaster response agencies.

3. Optimising wind farm locations in data-scarce regions

Wind power is a pretty important part of the global transition to renewable energy, but building a wind farm is an incredibly expensive long-term investment. To ensure a wind farm is economically viable, planners need to carefully select sites based on a bunch of technical, environmental, and socio-economic factors. Typically, researchers use Multi-Criteria Decision-Making (MCDM) tools, which rely on expert opinions to weigh factors like wind speed, terrain slope, and proximity to roads. However, these methods are pretty static, pretty subjective, and unable to predict the suitability of locations that haven’t been directly sampled. A new paper from Scientific Reports introduces a new framework that uses expert knowledge, objective efficiency analysis, and predictive machine learning to identify the best wind farm locations in the data-scarce Amhara Region of Ethiopia.

The framework operates in three stages. First, a Fuzzy Analytic Hierarchy Process (FAHP) was used to gather and weight expert opinions, identifying wind speed and proximity to power grids as the most critical factors while smoothing out the vagueness inherent in human judgment. Second, Data Envelopment Analysis (DEA) was applied to objectively measure the efficiency of these candidate zones, pinpointing the North Shewa zone as the absolute best location for converting available land and wind into energy output. Finally, the researchers trained three machine learning models (Random Forest, SVM, and XGBoost) on the FAHP-DEA data. The Random Forest model proved to be the most accurate, learning the complex spatial patterns to predict wind farm suitability across a massive 31-million-pixel map of the region.

This integrated approach successfully delineated 1,698 square kilometres of high-priority land within North Shewa, divided into two distinct development corridors. These findings were strongly corroborated by independent engineering assessments and the Ethiopian government’s own development pipeline. By successfully combining subjective expert weighting, objective efficiency screening, and machine learning generalization, this framework provides a highly accurate, scalable blueprint for strategic wind energy planning that can be easily adapted for other developing regions worldwide.

4. A smart farming framework for pest management

Sustainable agriculture faces massive challenges from unpredictable climate changes, varying soil conditions, and sudden pest outbreaks. Precision agriculture aims to solve these problems by using sensors and AI to manage farms efficiently. However, models like Convolutional Neural Networks (CNNs) struggle to understand the complex, real-world relationships between weather, soil, and crops. Furthermore, centralising data from multiple farms onto a single server creates privacy and bandwidth issues. To tackle these challenges, a team of researchers developed GNN-ML-FRL, a highly advanced, decentralised AI framework designed to identify pests and model environmental factors across diverse farming regions.

This new framework combines three techniques. First, Graph Neural Networks (GNNs) map out the spatial relationships between different sensors and farm plots, allowing the system to understand how a pest outbreak in one area might affect a neighbouring field. Second, Federated Learning (FL) enables different farms to collaboratively train the AI without ever sharing their private, raw data with a central server. Finally, Meta-Learning (ML) and Reinforcement Learning (RL) allow the system to rapidly adapt to new, unseen environmental conditions and make real-time decisions, such as optimising irrigation or pesticide use.

When tested against a massive dataset containing over 52 million environmental sensor readings and 75,000 images of insect pests, the GNN-ML-FRL framework outperformed all standard AI baselines. It achieved an 89.3% accuracy rate in pest identification and demonstrated a significant reduction in prediction errors across geographically unseen farms. The researchers also used the framework to analyse genetic crop data, proving its versatility. Ultimately, this scalable, privacy-preserving AI system offers a robust blueprint for the future of sustainable, data-driven farming. You can access the code here.

Geospatial Datasets

1. Global agricultural monitoring and field boundary dataset

The collection integrates a multi-source framework that combines high-resolution spatial mapping with continuous biophysical tracking to capture field-level dynamics and macro-scale crop conditions. This maps over 3 billion agricultural field units at a 10-metre resolution across 241 countries and territories. You can access the FOTW project here and the GEOGLAM Crop Monitor here.

2. High-resolution canopy height dataset

The NAIP-CHM dataset provides a continuous 0.6-metre resolution canopy height and vertical structure model across the US. Derived from National Agriculture Imagery Program aerial imagery spanning 2012 to 2023, the dataset characterizes the full vertical profile of the landscape, mapping both natural vegetation heights and human-made infrastructure. You can access the data here and the code here.

3. Text-guided object counting dataset

The Cross-domain Large-scale Object Counting (CLOC) dataset is a text-guided object counting collection designed to evaluate generalist vision models across diverse imaging conditions and density distributions. It unifies multi-source heterogeneous annotations from public repositories into a standardized category-specified format, containing approximately 220,000 images, 619 categories, and 15 million instance point annotations. You can check out the project page here and the code here.

4. Earthquake high-frequency magnitude catalogue

The Italian Catalog of Earthquakes based on the high-frequency earthquake Magnitude m3Hz (ICEM) provides high-frequency magnitude records for 1,951 historical and recent Italian earthquakes spanning from 1117 to 2020. You can access the data here.

Other useful bits

• Google Earth has introduced new artificial intelligence-powered data layers that extract infrastructure assets like fire hydrants and storm drains directly from Street View imagery. Available for Professional and Professional Advanced users on web and Android, these layers combine with updated building footprints and land parcels to allow remote inspection of utility access and site constraints.

• An open-source tool called SplatAnalyzer utilises cloud inference to convert 3D Gaussian splats into structured data by generating 3D bounding boxes and object labels in under a minute. Designed for applications in VFX, gaming, digital twins, and physical AI, the interactive kit allows users to isolate, highlight, and manipulate specific assets within a scene without needing local compute.

• The Environmental Defenders Office has launched an interactive map on World Oceans Day to help the Australian public track offshore fossil fuel expansion in their waters. The tool outlines the environmental risks of deep-sea drilling and seismic blasting to marine biodiversity, while providing users with information on how to exercise their legal rights in environmental decision-making.

Jobs

• SEI is looking for a Mid–senior Researcher in data systems and digital governance based in Stockholm.

• EIB is looking for a Data Warehouse Officer based in Luxembourg.

• PAHO is looking for a remote GIS and Information Products Consultant.

• Vizzuality is looking for a remote Software Engineer based in either Spain, Portugal or UK.

• GiveDirectly is looking for a Remote Sensing Analyst based in New York.

Just for Fun

Hold on to your horses, NASA’s Voyager 1 will cross the 1 Light-Day threshold on November 18, 2026. That’s about 16 billion miles from Earth. If you use marathons as your unit of measurement, that’s like 610 million marathons.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — we’re sort of like Playboy for geospatial nerds. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Rice Monitoring: Drones improve crop health estimation.

• Generative Earth Observation: COP-GEN captures real-world variability.

• Damage Assessment: 3D disaster dataset for structural damage.

• Levee Erosion: Multi-scale wave overtopping dataset.

• Ocean Nutrients: Warming seas increase nutrient stress.

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Research you should know about

1. Taking precision agriculture to new heights

If, like me, you live in Asia, then you know that rice is kinda a big deal. Monitoring the Leaf Area Index (LAI) of rice crops is an important part of understanding their health and water use efficiency. Farmers and researchers traditionally measure this by hand, which is incredibly slow, labour-intensive, and difficult to scale across massive agricultural operations. To speed things up, scientists are increasingly turning to drones equipped with advanced cameras. However, standard drone imagery often struggles with changes in lighting or gets confused by the soil background when the crop canopy is either too thin or too dense. A new paper from Scientific Reports tries to address this by blending multiple types of drone data to create a highly accurate monitoring framework specifically tailored for rice during its critical booting stage.

The research team flew drones equipped with both standard RGB and multispectral cameras over rice fields at altitudes of 30 and 60 metres. Instead of just looking at basic colours, they extracted a complex mix of colour indices, spectral vegetation indices, and spatial texture features. They also developed new texture feature indices that specifically couple spatial patterns with spectral data. When they fed this fused data into six different machine learning models, the results were highly impressive. They found that flying at a lower 30-metre altitude and combining colour, vegetation, and the new texture indices yielded the best outcomes. Using this specific combination, a Convolutional Neural Network and a Random Forest model achieved an accuracy score of over 90%, proving that blending spatial and spectral data easily overcomes the blind spots of using just one type of sensor.

These findings provide a pretty interesting solution for those looking to implement precision agriculture. By confirming the ideal flight altitude and the exact mix of data features needed, the researchers have created a reliable blueprint for tracking crop health from the sky. Interestingly, the study also noted that standard machine learning models like Random Forest performed incredibly well given the relatively small sample size, sometimes even outpacing more complex deep learning networks.

2. Cop-Gen: A generative approach to Earth observation

In the geospatial space (or ‘geospace’, if you will), satellites gather petabytes of data daily, capturing everything from optical imagery and radar to terrain elevation and land cover. The challenge lies in integrating these diverse “modalities” into a single, cohesive system. Traditionally, we’ve relied on deterministic models, which take a set of inputs and produce a single, averaged “best guess” output. However, the real world is obviously pretty ambiguous. A single type of terrain or land cover could look pretty different depending on the weather, the season, or the lighting. By forcing a single output, these models fail to capture the true variability of Earth’s physical systems.

Researchers built COP-GEN to address this: a new multimodal generative model designed to handle the massive influx of data from the Copernicus satellite program. COP-GEN uses a “latent diffusion transformer” architecture, which allows it to process all the different types of satellite data at their native resolutions simultaneously. Instead of producing one averaged image, COP-GEN acts as a stochastic model, meaning it generates multiple, physically plausible versions of a scene based on the input data. The model can even perform “zero-shot” translation, meaning it can fill in missing data (e.g. generating a realistic optical image using only radar and elevation data) without needing to be retrained for that specific task.

When tested against a massive global dataset, COP-GEN proved pretty good at capturing the natural variance of the Earth’s surface. In a head-to-head comparison with a leading deterministic model, COP-GEN was able to cover 90% of the possible real-world variations of a scene, whereas the competing model collapsed to just 2.8%, producing the same image over and over. You can access the code here.

Geospatial Datasets

1. Post-disaster structural damage 3D dataset

The 3DAeroRelief dataset provides the first 3D benchmark dataset specifically designed for large-scale outdoor post-disaster structural damage assessment. It comprises dense 3D point clouds reconstructed via Structure-from-Motion and Multi-View Stereo techniques using optical RGB imagery captured by low-cost unmanned aerial vehicles over hurricane-damaged areas. You can access the data here and the code here.

2. Levee erosion and wavefront velocity dataset

The collection provides a multi-scale dataset compiled from four experimental field campaigns and corresponding laboratory tests designed to evaluate the spatiotemporal erosion resistance of earthen levee cohesive clay covers under wave overtopping loads. You can access the data here and the code here.

3. Glacier inventory dataset

The Tajikistan Glacier Inventory (TGI) provides a national-scale glacier dataset for Tajikistan derived from 3-metre resolution PlanetScope satellite imagery acquired in 2024. The collection maps 12,325 glaciers covering a total area of 7,720.26 square kilometres. You can access the data here and the code here.

Other useful bits

• A new study combining two decades of NASA Aqua satellite data with global ocean surveys has mapped nutrient stress in marine microorganisms caused by warming waters. By tracking shifts in the carbon-to-chlorophyll ratio of phytoplankton alongside genetic markers, researchers found that ocean stratification is increasingly limiting nutrient availability, particularly in subtropical gyres.

• The EU is investing billions of dollars in several large-scale rail tunnels to bypass natural geographic barriers and shift passenger and freight traffic from air and road to rail. While mega-projects like the Gotthard, Brenner, and Mont Cenis base tunnels promise to significantly reduce travel times across the continent, they face substantial construction delays, ballooning costs, and a lack of completed connecting lines from neighboring countries.

• A recent article addresses the challenge of building machine learning models in geospatial applications where satellite data is abundant but accurate field labels are rare and expensive. The author outlines a practical workflow for small environmental datasets, highlighting that rigorous spatial validation and transparent mapping of uncertainty are critical to avoid artificial inflation of model accuracy.

• ESA has awarded a contract to Thales Alenia Space and Airbus Defence and Space to build two Copernicus Sentinel-1 Next Generation satellites to ensure long-term continuity for Europe’s radar Earth observation capabilities. The new satellites will feature advanced C-band synthetic aperture radars that extend coverage to the polar regions and offer a fourfold improvement in geometric resolution compared to the first-generation mission.

Jobs

• PAHO is looking for a remote GIS and Information Products Consultant.

• Vizzuality is looking for a remote Software Engineer based in either Spain, Portugal or UK.

• IOM is looking for a GIS Manager based in Berlin.

• GiveDirectly is looking for a Remote Sensing Analyst based in New York.

• UNITAR is looking for (1) Geospatial Analysts - Satellite Imagery and GIS and (2) Geospatial Quality, Assurance, Training and Analytical Support Specialists based in Geneva.

• WFP is looking for a Geospatial Data Engineer based in Rome.

Just for Fun

A time-lapse captured from a SpaceX Dragon spacecraft shows a spectacular southern aurora caused by a recent solar event, with the phenomenon dancing directly below the vehicle.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a safe space for the cartographically curious. As usual, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Poverty Mapping: Tempov updates wealth maps using satellites.

• Thermal Imaging: Dual AI models sharpen infrared imagery.

• Satellite Training: Pixel diversity improves foundation model performance.

• Weather Extremes: AI forecasts struggle beyond past records.

• Nightlights: NASA reveals shifting global illumination patterns.

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Research you should know about

1. Stanford’s foundation model for predicting poverty

Traditional methods for measuring poverty in developing nations rely on household surveys and census data. These methods can be expensive, slow, and infrequent, which leaves policymakers working with outdated information when deciding where to allocate resources or build infrastructure. Over the last decade, researchers have started using daytime satellite imagery to try to measure economic activity, looking for visual proxies like new roads or agricultural expansion. While this works well for a static snapshot of an area, these models often struggle to track changes in wealth over time, confusing seasonal changes like fields turning brown in the winter for actual economic decline.

To fix this, a team of researchers built Tempov, a new geospatial foundation model specifically designed to track long-term economic changes (more info on foundation models here). The model was trained on a massive dataset of three million satellite images spanning two decades. Interestingly, the researchers intentionally fed the model ‘bi-temporal’ pairs, i.e. pictures of the exact same location taken during different seasons of the same year. This forced the model to ignore transient, seasonal changes like snow or dead crops and focus entirely on permanent, human-made changes like new buildings or roads.

Tempov proved to be pretty effective. The researchers demonstrated that if Tempov was given a decade-old census from a country, it could accurately predict the modern wealth distribution using only 5% of modern survey data. In resource-strapped nations, this means governments can achieve highly accurate poverty mapping at a fraction of the cost of a full census. To prove its scalability, the team deployed Tempov to map the entire continent of Africa, producing a high-resolution, 6km-by-6km grid showing the exact changes in wealth from 2015 to 2025. This open-source tool could dramatically change how we monitor and combat global poverty.

2. Sharpening the world of thermal imaging

Enhancing the resolution of remote sensing imagery can be pretty important for applications like autonomous driving and environmental monitoring. But Super-resolution for infrared or thermal images brings a specialised set of challenges. Unlike standard visible light photographs, thermal images lack rich textures and rely heavily on clear object outlines and stable regional temperatures. If a super-resolution system sharpens the image too aggressively, it can create fake hot spots or unstable temperature readings that ruin the utility of the data.

To tackle this, a team of researchers developed a dual-branch framework for the NTIRE 2026 Infrared Image Super-Resolution Challenge. Instead of forcing one neural network to do everything, they combined two different architectures to capture the best of both worlds. The first branch uses a transformer model called HAT-L to focus on sharpening local edges and fine contours. The second branch employs a state-space model named MambaIRv2-L to handle the big picture, ensuring long-range temperature consistency across the entire scene. The system processes the low-resolution images through both branches independently, applies specific test-time enhancements to each, and simply averages the two outputs together to create the final image.

This collaborative approach proved pretty successful in generating clear and accurate thermal imagery. By blending the aggressive edge recovery of the transformer with the global stability of the state-space model, the fused output comfortably beat either model working on its own. The researchers evaluated their system using the official NTIRE 2026 development and hidden test sets, alongside a custom evaluation batch derived from the Caltech Aerial RGB-Thermal dataset.

3. Mapping out the best data for satellite AI

As I mentioned above, in recent years, developers have built massive ‘foundation models’ designed to analyse satellite imagery for tasks like mapping populations, tracking floods, or estimating crop yields. To train these models, researchers use huge datasets of unlabelled satellite images, a process known as pretraining. However, there are no real standards for how this data is collected. Some models are trained exclusively on images of European cities, some are trained on a perfectly even grid of the entire Earth, and others focus heavily on specific biomes like deserts or forests. Until now, no one had rigorously tested how these different geographic sampling strategies actually affect the AI’s final performance.

To solve this, researchers built seven distinct pretraining datasets (six covering individual continents and one global mix) and used them to train identical foundation models. They then tested these models on a variety of global and local mapping tasks. The results were highly surprising. The model trained exclusively on images of Europe consistently outperformed all the others, including the globally trained model, across almost every single task. Even when the AI was asked to map farms in Africa or populations in South America, the Europe-trained model proved to be the most accurate.

To figure out why the European dataset performed so well, the researchers measured the ‘diversity’ of all the datasets across different metrics. They discovered that simply having a wide variety of continents, biomes, or landcover types in the training data didn’t actually boost performance. What mattered most was ‘spectral diversity’, which measures the sheer complexity and variety of colour and light values within each individual image. Because the European images happened to have the highest spectral complexity, they provided the best training ground for the AI, proving that when it comes to training satellite models, the richness of the pixels matters far more than the geographic coordinates they came from.

4. AI vs Physics: Forecasting extreme weather

Geospatial models have begun to target weather forecasting, often outperforming leading physics-based numerical models on standard benchmarks. Models like GraphCast and Pangu-Weather are faster, more energy-efficient, and generally provide more accurate daily forecasts. However, scientists have questioned whether these data-driven models can reliably predict unprecedented, record-breaking weather events. Unlike physics-based models that calculate the actual laws of thermodynamics and fluid dynamics, AI models rely entirely on finding patterns in historical training data. A new study from Science Advances put this to the test by evaluating how well leading AI models could predict record-breaking heatwaves, cold snaps, and windstorms compared to the gold-standard physics-based model, the European Centre for Medium-Range Weather Forecasts’ HRES.

The researchers built a massive dataset of record-breaking weather events from the years 2018 and 2020. These events were specifically chosen because they exceeded any measurement recorded in the AI models’ training data (which covered 1979-2017). When tested on these unprecedented extremes, the AI models consistently failed, significantly underperforming the physics-based HRES model. The AI models not only failed to predict the frequency of these record-breaking events, missing many of them entirely, but they also severely underestimated their intensity.

Importantly, the researchers found that the larger the extreme event was compared to historical records, the worse the AI’s prediction became. It was as if the AI models hit a soft “ceiling” based on their training data and simply could not imagine an event being any worse.

These findings highlight a dangerous “extrapolation” problem in current AI weather models. Because they are not grounded in the actual laws of physics, they struggle to predict events that fall outside of their historical training data. While AI offers incredible speed and general accuracy, this study proves that we cannot solely rely on them for early warning systems or disaster management, particularly in a rapidly warming climate where unprecedented extremes are becoming the new normal. For now, physics-based models remain essential for keeping communities safe from the worst that nature can throw at them.

Geospatial Datasets

1. European agricultural land use systems map

This collection provides a harmonised, Europe-wide agricultural land use systems map at a 10-m spatial resolution covering the period from 2017 to 2021. You can access the data and code here.

2. Global sea and sea-Ice surface temperature dataset

This dataset introduces a 43-year Climate Data Record providing daily, gap-free global fields of combined sea surface temperature and sea-ice surface temperature at a 0.05-degree spatial resolution from 1982 to 2024. You can access the data here and the code here.

3. Pan-Arctic super-resolved land surface temperature dataset

A new 42-year climate data record offers twice-daily, clear-sky land surface temperature (LST) data for the entire pan-Arctic region at a 1-km spatial resolution from 1982 to 2023. You can access the data here.

4. Global coastal soil carbon accumulation dataset

A new global dataset provides a comprehensive inventory of soil organic carbon accumulation rates within coastal wetlands. This open-access resource contains 8,998 standardised field observations derived from a systematic review of 247 peer-reviewed articles published between 1981 and 2025. You can access the data and code here.

Other useful bits

• New imagery from the NISAR satellite has revealed that Mexico City is sinking at an alarming rate of up to 0.8 inches a month due to the over-pumping of its underlying aquifer. It captures subtle ground movements in detail, providing some pretty shocking insights to help the city manage its infrastructure and pressing water crisis.

• As the number of active satellites skyrockets, the science academies of G7 countries have called for the creation of an Intergovernmental Panel on Space Sustainability (IPSS). This initiative aims to establish international standards and global thresholds to protect the night sky, manage orbital space debris, and balance commercial innovation with environmental safety before a critical tipping point is reached.

• By analysing a decade of data from its "Black Marble" program, NASA revealed that the Earth's nighttime lighting is highly dynamic and flickers with industrial shifts. The study shows how changes in artificial light trace real-world human stories, from economic booms and urban development to energy crises and green policy shifts. And if you’re a nightlights nerd, you might want to check this out.

Jobs

• WFP is looking for a Geospatial Data Engineer based in Rome.

• UNICEF is looking for a GIS Lead based in Florence.

• GiveDirectly is looking for a Remote Sensing Analyst based in New York.

• UNITAR is looking for (1) Geospatial Analysts - Satellite Imagery and GIS and (2) Geospatial Quality, Assurance, Training and Analytical Support Specialists based in Geneva.

• WWF is looking for a Senior Conservation Data Analyst/Engineer based in Washington, DC.

Just for Fun

Jupiter and Venus, on 9 June, had the appearance of being pretty close together. This is known as a ‘planetary conjunction.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — your weekly map to the latest geospatial news. We buffer out the noise, snap to the most interesting features, and provide geospatial puns that are even worse than the GeoDA UI. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Trade Deaths: Global trade shifts pollution deaths abroad.

• Political Violence: Conflict scars the next generation.

• EO Embeddings: Compact vectors streamline satellite analysis.

• SAR Matching: Off-the-shelf AI aligns disaster images

• Climate Pledges: ClimActor tracks subnational climate action.

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Research you should know about

1. The hidden health costs of global trade

If you’ve ever been to a city, or read about cities in books, or seen a city in a movie, then you might have experienced the joys of air pollution from fine particulate matter (PM2.5). It causes around 5.1 million deaths a year. While it’s easy to blame the countries where this pollution is emitted, the reality of global economics paints a bit more of a complicated picture. When a wealthy country imports cheap, manufactured goods, it’s effectively outsourcing the dirty, dangerous industrial work required to make them. This is basically the idea of ‘imported emissions’. Until now, scientists have struggled to map exactly how this international trade shifts the health burden of air pollution from wealthy nations to poorer ones on a country-by-country basis.

A new study from Nature Communications quantifies this by combining global economic data, air pollution transport models, and epidemiological studies. The researchers discovered that roughly 40% to 48% of all economy-related PM2.5 deaths are caused by the production of goods meant for export. Most shockingly, 14% to 18% of all global air pollution deaths occur when a wealthy country (with a high GDP) imports goods from a poorer country (with a GDP at least 50% lower). This means that high-income nations like the US and the UK are effectively exporting thousands of pollution-related mortalities to countries like China, India, and Bangladesh in exchange for cheaper goods.

This massive imbalance exposes a major flaw in how governments currently calculate the ‘externality costs’ of pollution. In countries like the US, the economic cost of pollution is only calculated based on the lives of its own citizens. If a US policy causes pollution deaths in another country, those lives are valued at zero, essentially incentivising companies to offshore their most toxic practices to the poorest nations. The researchers propose a new ‘Fair Trade in Pollution’ model that forces wealthy nations to value the lives lost in producing countries as highly as their own citizens, a shift that would drastically alter the math behind international trade policies. You can access the code for this study here.

2. The generational toll of political violence

We all know that armed conflict and political violence cause massive, immediate suffering. However, much less is known about the ripple effects these events have on the daily lives of children and young adults long after the dust settles. When a community experiences political violence, the resulting trauma, economic instability, and breakdown of local services can create a culture of insecurity. In these high-stress environments, violence is often normalised as a way to resolve disputes, leading to an increase in interpersonal violence within families and romantic relationships.

To measure this phenomenon, researchers built a massive dataset combining the Violence Against Children and Youth Surveys (VACS) with the Armed Conflict Location and Event Data (ACLED) project. By cross-referencing these two databases, they were able to track the history of political violence across nine African nations and compare it directly to self-reported instances of interpersonal violence against adolescents and young adults. The results showed a stark, long-term connection. If a community experienced a significant increase in political violence over a 15-year period, the local youth were 5.5% more likely to experience emotional violence from family members and 3.0% more likely to suffer physical violence from an intimate partner. Notably, the study found no significant link between short-term political violence and increased youth violence, proving that this is a generational, slow-burning issue.

The study also highlighted how economic factors exacerbate these dangers, with young people in poorer households being 16.9% more likely to experience sexual violence following long-term political instability. These findings prove that the scars of political violence extend far beyond the battlefield, infecting the homes and relationships of the next generation. You can access the data and code here.

3. Optimising Earth observation with compact embeddings

Earth observation satellites capture petabytes of multispectral imagery every day, which researchers increasingly analyse using large Geospatial Foundation Models. Instead of constantly feeding raw data through these massive models for every new task, scientists are shifting towards an embedding-centric approach. In this setup, models process the imagery once to create compact, reusable vector representations known as embeddings. However, deciding exactly how to compress this data, such as which layers of the model to use or how to pool the information, significantly impacts performance. A new study from IBM systematically evaluates these design choices to help developers build highly efficient Earth observation pipelines without sacrificing accuracy.

Using an evaluation framework called NeuCo-Bench, the researchers tested various embedding configurations across multiple tasks, including land cover classification, biomass estimation, and cloud cover prediction. They discovered that Vision Transformers consistently outperformed traditional convolutional networks like ResNet when dealing with continuous environmental variables. Furthermore, simply taking the average of the spatial features, a technique known as mean pooling, proved to be the most robust aggregation strategy across the board. The team also noted that while transformer models perform best at their final layers, convolutional networks actually produce their most useful embeddings at intermediate stages, meaning the default practice of using the final layer is often a mistake.

The findings show that carefully designed, fixed-size embeddings can compress raw satellite data by over 500 times while remaining incredibly useful for a wide range of analytical tasks. The study also highlights that different training methods excel at different tasks, meaning that combining embeddings from multiple models can further boost overall reliability. By following these design guidelines, developers can create scalable, lightweight systems for monitoring our planet.

4. Can off-the-shelf AI match optical and SAR satellite imagery?

When a disaster like a hurricane strikes, thick cloud cover often makes standard optical satellite imagery useless. In these situations, responders rely on Synthetic Aperture Radar (more info on SAR here) which can see through clouds but produces grainy, distorted images that look radically different from regular photos. To quickly assess damage, analysts need to perfectly align, or “register,” these new SAR images with older optical basemaps. It’s a pretty notoriously difficult task because the two types of imagery are fundamentally different. While there are plenty of highly advanced AI image-matching tools available, almost all of them were trained on normal, ground-level photos. A new study tries to see whether these general-purpose AI matchers actually handle the extreme differences between overhead optical and SAR satellite imagery.

To find out, a new paper tested 24 different pretrained AI matchers on SpaceNet9, a dataset of paired optical and SAR satellite images. They evaluated the models in a “zero-shot” setting, meaning the AIs had to perform the task straight out of the box without any special training or fine-tuning for satellite data. The results were surprising. While models specifically trained for cross-modal tasks (like matching visible light to thermal images) performed exceptionally well, some general-purpose models performed almost identically. For example, a model called RoMa, which was only ever trained on normal photos, achieved the lowest error rate alongside the cross-modal specialist XoFTR. This suggests that the sheer size and diversity of data used to train modern foundation models gives them an unexpected ability to bridge the gap between radically different types of imagery.

However, the study also revealed that picking the “best” AI model is only half the battle. The researchers found that how the model is deployed (specifically the geometric math used to align the images and how the images are cropped into tiles) often mattered more than the choice of AI itself. E.g., simply forcing the models to use an “affine” geometry model (which assumes the ground is relatively flat) rather than a “homography” model (which accounts for complex 3D perspective) slashed the error rate by quite a lot. The key takeaway for disaster responders is that while off-the-shelf AI matchers are surprisingly capable of handling satellite data, they require careful, domain-specific configuration to be truly reliable in the field.

Geospatial Datasets

1. Global agricultural field boundary map

The PRUE global dataset delivers the very first wall-to-wall map of agricultural field boundaries at a pretty granular 10-m resolution for 2024 and 2025. You can access the data here and code here.

2. Spatiotemporal hyperspectral EO dataset

The ChronoEarth-492K dataset introduces the first large-scale, temporally calibrated hyperspectral self-supervised learning collection, built on NASA’s EO-1 Hyperion mission archive spanning from 2001 to 2017. You can access the data and code here.

3. Climate pledges and emissions dataset

The ClimActor 2.0 database provides an expanded, openly available geospatial resource that maps climate targets, action plans, and emission inventories from subnational governments worldwide. You can access the data here and the code here.

4. National-scale sandy beach dataset

This new dataset introduces the first comprehensive, high-resolution national map of sandy beaches along the extensive 11,098.81 km coastline of India.

Other useful bits

• The creators of the Global Automation Atlas have mapped how automation will unfold across 124 countries by comparing over 18,000 distinct tasks. This new tool provides an incredible 2.3 million task-country comparisons.

• The new GeoX framework is impacting geospatial reasoning by using a self-play mechanism that allows AI to teach itself complex spatial logic without the need for costly human data. By generating and solving its own programming problems, this system creates a virtuous cycle of autonomous improvement that pushes vision-language models to state-of-the-art performance.

• NASA and IBM’s open-source Prithvi model has become the very first AI geospatial foundation model to be successfully deployed in orbit. By running directly on the International Space Station and the Kanyini satellite, the model system can quickly analyse floods and wildfires from space, cleverly saving massive amounts of bandwidth by processing Earth observation data before it even hits the ground.

• In the wake of Hurricane Melissa, Jamaica's National Spatial Data Management Branch demonstrated the power of resilient geospatial infrastructure by mapping nearly 200,000 damaged buildings within days. The nation used its National Geospatial Repository and partnered with international tech organisations to create a shared real-time dashboard, allowing emergency teams to coordinate relief and restore essential services with incredible precision.

Jobs

• UNITAR is looking for (1) Geospatial Analysts - Satellite Imagery and GIS and (2) Geospatial Quality, Assurance, Training and Analytical Support Specialists based in Geneva.

• WWF is looking for a Senior Conservation Data Analyst/Engineer based in Washington, DC.

• Google is looking for a Technical Program Manager under their Geo team based in Singapore.

• UNODC is looking for a Geospatial Information Management Intern based in Vienna.

Just for Fun

The Sentinel-6 Michael Freilich satellite has detected a massive swell of warm water moving across the Pacific Ocean, which means that El Niño will likely arrive later this year.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a place where you aren’t judged for being a little sinusoidal... Anyway, the aim as usual is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• AI Image Search: ProVG improves satellite object detection.

• Farm Boundaries: PRUE maps fields at global scale.

• Car Dependency: New index maps urban transport reliance.

• Carbon Credits: REDD+ projects show major over-crediting.

• Ocean Data: WOD23 compiles centuries of ocean measurements.

• Photosynthesis Data: TRAX GPP maps global productivity.

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Research you should know about

1. Teaching AI to search satellite images like a human

A new study introduces ProVG, an AI framework designed to dramatically improve object detection in satellite imagery based on text descriptions. Spotting a specific target in a remote sensing image can be pretty difficult because the scenes are massive and packed with identical-looking objects. Usually, AI models try to find a target by processing the user’s entire text prompt as a single chunk. It turns out this approach isn’t great. When a model processes a sentence all at once, it doesn’t pick up on fine-grained clues like spatial relationships or specific physical attributes, which often leads to the model highlighting the wrong object entirely.

To fix this, the researchers looked at how human brains actually process visual searches and built a progressive framework that mimics our natural cognition. Instead of swallowing a sentence whole, ProVG breaks the text prompt down into three distinct pieces: (1) global context, (2) spatial relations, and (3) object attributes. The model then applies these clues in a strict sequence to filter the image. First, it surveys the global context to get a general understanding of the scene. Next, it uses spatial clues to narrow down the search to a specific neighbourhood. Finally, it verifies the exact target by matching the specific physical attributes.

Alongside this structured searching method, the framework also includes special modules to handle the extreme scale variations found in satellite photos, helping it fuse details from both zoomed-in and zoomed-out perspectives. The researchers tested ProVG against a huge lineup of existing models across two major remote sensing benchmarks. The results were pretty impressive. By simply decoupling the language and forcing the model to search step-by-step, ProVG consistently outperformed current top-tier systems, proving to be incredibly accurate at drawing both basic bounding boxes and precise pixel-level masks around targets.

2. Mapping the world’s farms from space

Knowing exactly where farm fields begin and end can be pretty important for tracking crop yields, managing water resources, and monitoring climate initiatives. There are too many approaches to name that uses AI to automatically draw these boundaries by scanning satellite images. However, when these models are taken out of the lab and deployed in the real world, there can be a lot of issues… A model that works perfectly in the sunny plains of Illinois might completely mess up when scanning the cloudy, mountainous terraces of Japan, drawing jittery lines or hallucinating extra fields.

To solve this, researchers built PRUE (Practical Recipe for Field Boundary Segmentation at Scale). They tested 18 different AI architectures against a massive global dataset of fields to see what actually works. This involved introducing new, real-world tests to measure how the AI handled changes in brightness, picture scaling, and the order of the images it was fed. They discovered that while some of the largest, most hyped ‘foundation models’ struggled, a specialized U-Net architecture crushed the competition. By feeding this model mathematically enhanced images and tweaking how it penalises mistakes during training, the researchers created an incredibly robust system.

The results speak for themselves. PRUE scored 6% higher in overall accuracy than previous top-tier models and proved remarkably stable when the lighting or image scale was tweaked. To prove it works at scale, the researchers used PRUE to map out every single agricultural field in Japan, Mexico, Rwanda, South Africa, and Switzerland, covering 4.7 million square kilometres in just a few hours.

3. Decoding car dependency in modern cities

To reach net-zero emissions, urban planners are pushing for car-free zones, better cycling infrastructure, and improved public transport. However, moving away from private vehicles is incredibly difficult because decades of car-centric urban growth have transformed the automobile from a convenience into an absolute necessity. For many residents, giving up their car means losing access to essential services, leisure activities, and jobs. A new study addresses this by quantifying exactly how reliant different neighbourhoods are on private vehicles compared to public transport.

A team of researchers developed the Car Dependency Index to map the accessibility gap between driving and taking public transport across 18 major cities in Europe and North America. By crunching high-resolution geospatial data, they calculated how many points of interest a resident could reach within a normal journey time using either a car or a bus and train network. The data revealed a lot of spatial inequalities, with peripheral districts almost entirely trapped in high-dependency cycles while central areas were far better equipped for car-free living. When looking closely at Vienna, the team also discovered that this neighbourhood dependency was a massive driver of car ownership, completely independent of a household’s income level.

To see if infrastructure upgrades could fix the problem, the researchers simulated the impact of a planned metro expansion in Rome. While the new transit line successfully removed an estimated 60,000 commuting vehicles from the roads, the benefits were extremely localised to the people living right next to the new stations. This proves that building a single new train line is simply not enough to break the cycle of car dependency across an entire city. Ultimately, if policymakers want to dismantle car-based systems and build truly sustainable urban spaces, they need to commit to massive, network-wide public transport expansions rather than isolated projects.

4. The truth behind forest carbon over-crediting

Forest carbon credits, particularly REDD+ projects, are meant to fund tropical forest conservation. However, recent controversies revealed that many early projects issued far more credits than they actually earned, causing voluntary carbon markets to lose over a billion dollars in value. Some industry figures pushed back against these claims, arguing that independent evaluations used flawed global satellite data that simply missed the real deforestation. To settle the debate, a new study from Nature Communications synthesised data from six independent evaluations covering 44 REDD+ projects to see exactly where the discrepancies came from.

The results showed that while most projects did successfully reduce deforestation, they still claimed to have saved nearly 11 times more forest than they actually did. Importantly, the researchers proved that the use of global satellite data was not the problem; these tools actually detected more deforestation than the custom layers used by the projects themselves. Instead, the massive over-crediting stemmed from how projects chose their baseline reference areas. Projects routinely selected highly accessible, heavily degraded comparison zones that were far more vulnerable to logging than the actual protected areas. When combined with overly pessimistic predictive models, this made the projects look vastly more effective than they really were.

To ensure carbon markets genuinely benefit the climate, the researchers argue that the industry must abandon flexible, prediction-based baselines. Future credits must rely on rigorous, after-the-fact evaluations using independent methods to measure real-world impact. You can access the data here and the code here.

Geospatial Datasets

1. World ocean database

The World Ocean Database 2023 (WOD23) is a large digital collection of oceanographic in situ profile measurements covering the instrumental record from 1772 to 2022, with around 18.6 million water column profiles and 3.6 billion measurements across 27 physical and chemical variables, including temperature, salinity, and oxygen. You can access the data here.

2. High-res global GPP dataset

The newly introduced TRAX GPP dataset provides a 0.05° global record of terrestrial photosynthesis from 2001 to 2024, using a hybrid Long Short-Term Memory framework with seasonal-trend decomposition and atmospheric CO₂ assimilation constraints to capture carbon cycle dynamics and CO₂ fertilisation effects. You can access the data here and the code here.

3. Long-term soil analysis dataset

The Sol_Run dataset provides a seventeen-year record of soil analysis data for Réunion Island from 2008 to 2024, with more than 266,000 records from nearly 25,000 unique samples covering physical and chemical properties such as pH, nutrient content, and cation exchange capacity. You can access the data here.

4. High-res Mediterranean wildfire dataset

The CalWildFire dataset brings together eleven years of daily in situ wildfire and meteorological data for the Mediterranean region of southern Italy, combining ground-validated meteorological measurements, field-based burned area records, and calculated fire weather indices. You can access the data here.

Other useful bits

• Planet has launched SuperRes technology, a new AI-based super-resolution dataset that sharpens its satellite imagery from 3 metres to 2-metre resolution. The update makes small-scale details, such as rooftop markings and individual trees, easier to inspect while retaining Planet’s daily global monitoring coverage.

• Earth Index is officially live offering free global access to its AI-powered satellite search tools. The platform is already being used to identify issues such as illegal deforestation and hazardous quarries through large-scale satellite image search.

• Render a House has launched a new 3D mode that allows users to place their own 3D models directly within the Google Earth environment. The update makes it easier to see how proposed projects fit into their real-world surroundings and create more consistent project images.

Jobs

• World Vision is looking for a WASH Data Fellow based in the US.

• Google is looking for a Technical Program Manager under their Geo team based in Singapore.

• UNODC is looking for a Geospatial Information Management Intern based in Vienna.

• Esri is looking for a GIS Solution Engineer – Global Alliances & Partners based in San Antonio.

Just for Fun

Saturn and Neptune in retrograde, traced across 34 nights from May 2025 to February 2026. This composite shows Saturn as the brighter foreground planet and Neptune as the dimmer object behind it, both appearing to move backwards across the sky as Earth overtook them on its faster inner orbit around the Sun. Saturn moved from Pisces into Aquarius and back again, while Neptune remained in Pisces, during their closest apparent pairing since the Saturn-Neptune conjunction of 1989.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a safe space for map fetishists… The aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Disaster Damage: AI assesses buildings after natural disasters.

• Map Queries: LLMs make spatial databases conversational.

• Data Heat: AI infrastructure warms nearby landscapes.

• Satellite Labels: MAPLE teaches AI land-cover hierarchies.

• Urban Plastics: DURUM tracks microplastics in stormwater.

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Research you should know about

1. A sharper lens on disaster zones

A new study introduces a three-step AI framework designed to improve how we assess building damage from space. When a natural disaster strikes, quickly figuring out which buildings are destroyed is pretty important for sending rescue teams to the right places. However, the satellite imagery used for these assessments is often blurry or full of noise, which makes it pretty difficult for automated systems or even human experts to spot crucial details like collapsed roofs or cracked walls. Traditional object detection models can spot buildings but fail to interpret the severity of the damage, while newer Vision-Language Models (VLMs) can analyse scenes but often get confused and ‘hallucinate’ when looking at low-resolution photos.

To overcome these limitations, researchers from Dakota State University built a hybrid pipeline and tested it against the xBD dataset, a massive benchmark collection of satellite images covering various global disasters. The new framework tackles the problem in three steps. First, it uses a Video Restoration Transformer to artificially upscale the low-resolution satellite images, quadrupling their size to make structural details stand out. Next, it uses a YOLOv11 object detection model to draw precise boxes around every building in the image and crops them out. Finally, these high-resolution cropped images are fed into several different VLMs, which act as a ‘jury’ to assess the damage severity of each building on a four-point scale. By combining multiple models, the system drastically reduces the bias and hallucination risks of relying on just one AI.

When evaluated on imagery of Hurricane Matthew and the Moore Tornado from the xBD collection, the results showed that this multi-stage approach was pretty decent. By upscaling the images first, the VLMs were able to produce much more accurate and semantically rich descriptions of the damage. Furthermore, the team found that the Qwen3 family of models consistently outperformed Gemma3 models, proving to be far more reliable at identifying completely destroyed buildings. Ultimately, this pipeline could provide first responders with a faster way to prioritise rescue efforts without having to set foot in a hazardous area. However, I do remain sceptical about the super-resolution approach…

2. When maps learn to answer questions

Querying complex geographic or time-based databases usually requires some coding skills. If you want to find all the restaurants within a specific flood zone, you can’t just type that into a search bar and expect a database to understand you. Developers build Natural Language Interfaces to Databases so everyday users can bypass the complex code and just ask questions. A new paper maps out the entire landscape of this highly fragmented field, reviewing everything from the datasets used to the evolution of the algorithms themselves.

The paper breaks down how these systems have evolved across four main categories. Early models relied on strict, handcrafted grammar rules that worked well but struggled with casual or unexpected phrasing. Programmers then moved to hybrid neural networks that learned from data but still relied on rigid templates. Today, the focus has shifted entirely to LLMs and multi-agent systems, where different AI agents collaborate to interpret a query, check the database schema, and write the code. While these modern approaches can be incredibly flexible, they often suffer from geo-hallucination, where the AI invents imaginary spatial relationships or references tables that simply do not exist.

Beyond the models, the review highlights a pretty big issue with the data used to test them. Right now, there isn’t a single, unified benchmark that accurately reflects the messy and ambiguous way real people ask geographic questions. Instead, researchers rely on a mix of highly controlled synthetic datasets and real-world GIS databases that make it nearly impossible to compare different models fairly. Ultimately, the authors conclude that before we can build AI that truly understands space and time, the industry needs to ditch these fragmented tests and create a robust benchmark that accounts for real-world geographic ambiguity.

3. AI’s next environmental problem is heat

We hear a lot about the massive energy demands of artificial intelligence, but there’s a hidden side effect we rarely talk about: the heat. As tech companies scramble to build massive AI data centres across the globe, these power-hungry hubs are pumping enormous amounts of thermal energy into the atmosphere. To figure out the real-world impact, researchers combined two decades of satellite land surface temperature records with the exact locations of over 8,000 data centres built outside densely populated cities. This allowed them to isolate the specific temperature changes caused by these facilities without getting confused by typical urban warmth.

The data reveals a pretty crazy phenomenon that they call the ‘data heat island effect’. On average, the land surface temperature around a new AI hyperscaler spikes by 2°C right after it starts operating. This warming footprint isn’t just confined to the building itself. The heat ripples outward, raising local temperatures for up to 10 kilometres in every direction. When researchers overlaid this temperature data with global population maps, they discovered that over 340 million people currently live within these artificially warmed zones. It’s a pretty big deal, considering this extra heat places significant stress on local healthcare, energy grids, and water supplies.

Tackling this growing problem requires a mix of smart software and innovative hardware. On the coding side, developers can reduce compute loads by cleaning up messy data before processing or using stuff like carbon-aware token generation to improve efficiency. Physically, engineers are exploring some incredibly interesting tech to keep temperatures down. This includes adiabatic circuitry that actually recycles energy during computation, hybrid liquid cooling systems, and special passive radiative coatings that reflect solar heat away from the buildings. By combining these hardware and software upgrades, the industry has a real shot at keeping our environment cool(er) as AI continues to scale.

4. Teaching AI the family tree of satellite imagery

When AI models look at satellite imagery, it’s usually trained to spot flat, isolated labels like a ship, a tree, or a road. But the real world is pretty highly structured, and these labels actually belong to a complex hierarchy. E.g. a ship is part of a port area, which itself is a type of artificial surface. While some models try to learn these hierarchical relationships, they can get confused when a single image contains multiple objects from completely different branches of the taxonomic tree. Furthermore, these older methods are pretty computationally heavy and tend to fall apart when they do not have massive amounts of training data.

A recent paper introduces MAPLE, a new framework designed to solve this multi-path classification problem by explicitly teaching AI the hierarchical structure of remote sensing data. The system starts by feeding the model text-based descriptions of how different land cover types are related, creating a semantic map of the categories. It then uses a Vision Transformer to scan the satellite image and a Graph Convolutional Network to pass information along the branches of the label tree. Finally, it uses an adaptive fusion mechanism to dynamically weigh the visual evidence from the image against the structural rules, ensuring it makes logical predictions across every level of the hierarchy without any manual tuning.

The researchers tested MAPLE across several major remote sensing datasets that were mapped to standard land-cover classifications. The results were pretty impressive, particularly when the model was given very little data to learn from. In these restricted scenarios, MAPLE outperformed standard classification baselines by up to 42%, proving that understanding the relationships between categories gives models a massive head start. Interestingly, it achieved these gains while adding only a tiny 2.6% increase in computational overhead. The framework also proved quite effective at reducing dumb mistakes between tricky categories, making it a robust and highly efficient tool for Earth observation.

Geospatial Datasets

1. Wind tunnel gas dispersion dataset

The RED:VAPOR dataset captures gas sensor responses to vapour plumes in scale-model landscapes, using a wind tunnel fitted with both low-cost and advanced sensors to map synthetic gas dispersion across a dense 3D grid under controlled wind conditions. You can access the data here and the code here.

2. Urban runoff microplastics dataset

The Dataset of Urban RUnoff Microplastics (DURUM) brings together 180 urban stormwater microplastic sampling efforts across 15 countries from 2018 to 2024, covering runoff from streets, residential catchments, and industrial areas. You can access the data here and the code here. You can access the data here and the code here.

3. Multi-sensor EO dataset

The BigEarthNet.txt dataset provides more than 464,000 co-registered Sentinel-1 and Sentinel-2 satellite images paired with detailed textual descriptions, with nearly 9.6 million annotations covering land-use classes, spatial relationships, and visual question-answering pairs. You can access the data here.

4. Global soil water isotope dataset

This new dataset provides a harmonised global collection of soil-water stable isotope records from 1975 to 2024, bringing together more than 27,000 records from 463 observation sites across 37 countries and organising them into standard soil-depth intervals. You can access the data here.

Other useful bits

• While AI is often discussed in terms of its environmental footprint, DINOv3 is also being used for nature restoration finance. The model uses computer vision to count individual trees and measure canopy height from satellite imagery, helping investors verify local restoration projects at lower cost and direct funding towards farmers and communities.

• For the first time in history, agricultural field boundaries have been mapped at a global scale. The 10-metre resolution dataset is publicly available at no cost and can be explored through the FTW Explorer.

• Google Earth has just released an update that allows users to import Shapefiles and 3D models directly onto its real-world canvas. This improvement enables us to render industry-standard data as cloud-native layers, and they’ve even added elevation profiles to the measure tool to help you easily visualise detailed terrain data.

Jobs

• Yale University is looking for a GIS Research Specialist based in Connecticut.

• NTT Data is looking for a Esri/ArcGIS Specialist based in Arlington.

• BP is looking for a Senior GIS Specialist based in Denver.

• TomTom is looking for a Staff UX Designer under their Map Visualisation team based in Amsterdam.

• Esri is looking for a remote GIS Project Manager under their Assessment Department based in Canada.

Just for Fun

A research team aboard the Polarstern icebreaker has discovered a small, uncharted island in the Weddell Sea after sailing into a mysterious “danger zone” that was previously hidden from all nautical maps.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — your weekly map to the latest geospatial news. This newsletter is a bit more like RGB than SAR - we’re impacted by the weather, but we’re tried and true. The aim is to help you become a better geospatial data scientist in less than 5 minutes a week.

In today’s newsletter:

• AlphaEarth Insights: Embeddings reveal hidden land-cover structure.

• Disaster AI: Domain adaptation improves damage detection.

• Urban Planning: Deep learning unifies fragmented city data.

• 3D Remote Sensing: AI models finally learn elevation.

• Crop Adaptation: Climate-responsive planting strategies simulated globally.

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Research you should know about

1. Cracking open Google’s geospatial black box

I’ve previously covered Google’s AlphaEarth a few times on here. In short, it’s one of the new geospatial foundation models trying to turn satellite and Earth observation data into reusable “embeddings”. In simple terms, these embeddings are compressed numerical descriptions of places on Earth. The tricky part is that these numbers are usually hard to interpret. A new paper tries to open that black box by asking what the individual embedding dimensions seem to capture when applied to land cover classification.

The main finding is that the embedding space appears to have a kind of internal geography. Some dimensions act like specialists, strongly linked to one land cover type, such as permanent water, built-up areas, grassland, bare soil, or moss and lichen. These make intuitive sense because certain land covers have very distinctive spectral or structural signatures. Water, for example, has very low reflectance in near-infrared and shortwave infrared bands, while bare soil and sparse vegetation have a very different year-round pattern.

The more interesting part is that other dimensions are shared across multiple land cover types. These seem to capture messy transition zones: cropland and grassland, shrubland and tree cover, bare soil and wetlands, or snow and sparse vegetation. That’s useful because real landscapes rarely fit neatly into clean categories. The paper’s bigger point is that geospatial foundation models may be learning both the “core” of land cover classes and the fuzzy spaces between them. If that holds up across more tasks and regions, it could make these models easier to prune, explain, and use for things like change detection, land degradation monitoring, and environmental planning.

Another pretty interesting finding is that the embeddings do a lot of unnecessary heavy lifting. Because the researchers mapped out exactly what each dimension does, they found that you simply do not need to use all 64 of them. Depending on the specific terrain you are looking at, you can hit 98% accuracy using just two to twelve targeted dimensions. This means developers can prune away the dead weight and slash their computing and storage costs by up to 90%, making it far cheaper to run high-resolution global models without losing any meaningful detail.

2. Building AI that works across different disaster zones

When a major disaster strikes, emergency responders increasingly rely on AI to rapidly scan satellite imagery and assess building damage. This allows human operators to stop squinting at fuzzy photos and focus on deploying rescue teams. However, there is a fundamental flaw in how these systems are currently built. If an AI model is trained on satellite photos of an earthquake in Turkey, it could completely fail to detect damaged buildings from a hurricane in Louisiana. This phenomenon, known as ‘domain shift’, happens because the AI gets confused by different camera angles, weather conditions, or regional architecture, rendering the tool useless for new, unseen disasters.

To solve this, researchers experimented with a technique called Supervised Domain Adaptation (SDA). They took a top-tier AI model originally trained on a massive global dataset of various disasters and fine-tuned it using a small batch of specific data from Hurricane Ida. Rather than just relying on standard RGB satellite images, they also fed the AI a variety of mathematically enhanced pictures. They tested ‘unsharp masking’ to highlight tiny cracks and splintered roofs, ‘contrast enhancement’ to spot subtle water damage, and ‘edge detection’ to make the outlines of buildings stand out.

The researchers found that Domain Adaptation was absolutely essential; without it, the AI failed to identify severely destroyed buildings entirely. When paired with ‘unsharp masking’, the model achieved its absolute best results, boosting its ability to detect ‘major damage’ by 190% and ‘destroyed’ buildings by an incredible 341% compared to baseline models. Interestingly, the researchers found that combining all the image enhancements at once actually confused the AI, proving that when it comes to training robust disaster-response tools, less is often more.

3. A deep learning framework for holistic urban planning

As I’m sure you all know, OpenStreetMap is one of the most useful free geospatial datasets out there. And I’m also sure you know the main issue with it: quality varies a lot by place. Some cities have really detailed road networks, building footprints, land-use polygons and points of interest. Others are patchier, especially in fast-growing or less well-mapped areas. A new paper in Scientific Reports looks at whether deep learning can help turn OSM into something more useful for urban planning. The case study is Krasnodar, a rapidly growing city in southern Russia, where the authors combine OSM with Sentinel-2 imagery, demographic data, traffic sensors and air-quality measurements.

The main idea is to build a single workflow that handles several planning tasks at once. The authors use different models for different jobs: a CNN for land-use classification, U-Net for building footprint extraction, LSTM networks for traffic forecasting, and a hybrid CNN-RNN model for short-term air-quality prediction. The results are pretty strong. The land-use model reached 91.6% accuracy, the building footprint model reached 94.0% accuracy, the traffic model had an RMSE of 3.6 vehicles per hour, and the air-quality model had an RMSE of 2.3 µg/m³ for PM2.5 forecasting. The framework also showed that OSM performed best for roads and buildings, while points of interest were less reliable, likely because they depend more heavily on voluntary tagging.

The useful part of this paper is the shift away from treating urban datasets as separate layers. Building footprints help make sense of urban structure, land-use classification helps identify functional zones, traffic prediction captures mobility pressure, and air-quality modelling adds an environmental layer. Together, these outputs can support things like zoning updates, green-space planning, congestion management and pollution monitoring. There are still some clear limits: the study is based on one city, OSM quality is uneven, and deep learning models can be expensive for smaller planning departments to run. But the broader direction is interesting: free geospatial data, satellite imagery and relatively standard deep learning models can now be stitched together into a pretty practical urban intelligence system.

4. Teaching AI to see the third dimension in satellite imagery

When it comes to analysing satellite imagery, most modern Vision-Language Models (VLMs) have a massive blind spot: they can only see in 2D. While these models are fantastic at looking at a flat image and identifying forests, roads, and buildings, they have absolutely no concept of height or terrain. This is a pretty big problem if you are trying to use AI for things like predicting where a flood will hit or assessing landslide risks, where the physical slope and elevation of the land are far more important than what it looks like from above.

To fix this vertical blind spot, researchers built GeoHeight-Bench, a new testing framework specifically designed to see if AI can understand height. Because getting experts to manually annotate the elevation of thousands of buildings and hills is incredibly expensive, the team used an automated AI pipeline to generate the data instead. The resulting benchmark forces models to tackle a range of 3D problems, from simply retrieving the exact elevation of a specific pixel, to identifying the tallest building in a city, to predicting which areas of a map are most likely to flood based on the terrain.

To prove that AI can actually learn to see in 3D, the researchers also built their own model called GeoHeightChat. Instead of just feeding the AI raw optical images, they used a two-stage training process to teach it how to implicitly guess the geometric structure of a landscape just by looking at a standard 2D photo. When tested on the new benchmark, GeoHeightChat completely blew the competition out of the water, scoring 44.14% overall compared to GPT-4o’s 21.61%. This proves that by injecting a sense of vertical scale into these models, we can unlock a whole new level of practical, real-world problem-solving for remote sensing AI.

You can check out the project page here.

Geospatial Datasets

1. High-altitude wetlands map

This newly developed dataset presents the very first global, high-resolution (30 m x 30 m) map of high-altitude wetlands across the world’s major mountain regions. The researchers combined Sentinel imagery, elevation data, and global ecoregion maps with machine learning to identify more than 30,500 km² of wetland ecosystems. The dataset is useful because high-altitude wetlands remain poorly mapped despite their importance for water storage, runoff regulation, and ecosystem functioning, particularly across remote mountain regions such as the Himalayas and Andes. You can access the data here and the code here.

2. Watershed hydrometeorological dataset

The MACH dataset compiles hydrometeorological data across 1,014 US watersheds, combining daily meteorological forcings with streamflow observations over a 44-year period, with some catchments extending back as far as 75 years. It also includes watershed-level attributes such as geology, topography, land cover, and climate characteristics. You can access the data here and the code here.

3. Climate-responsive crop selection dataset

This dataset explores how changing crop varieties and planting schedules could help agriculture adapt to climate change by simulating crop yields and water use for maize, wheat, soybean, and rice between 1961 and 2100. One of the more interesting aspects is its focus on adaptation timing, showing how relatively small shifts in sowing dates can substantially affect water demand and crop performance under warming conditions. You can access the data here and the code here.

4. European Atlantic wave climate dataset

A new dataset provides high-resolution projections of future wave conditions along the European Atlantic coastline using regionally downscaled CMIP6 climate simulations. The researchers generated 3-hourly directional wave spectra for more than 1,000 offshore locations across both historical (1985 to 2014) and future (2030 to 2059) periods under two emissions scenarios, including both integrated wave parameters and full spectral energy information. You can access the data here and the code here.

Other useful bits

• An important new global assessment has applied the Red List of Ecosystems framework to mangroves for the first time, providing a detailed look at the pressures facing coastal ecosystems worldwide. The report finds that roughly half of global mangrove ecosystems are now considered at risk due to factors such as coastal development, sea-level rise, pollution, and changing sediment dynamics. Beyond the conservation implications, the assessment also highlights the growing role of geospatial monitoring in tracking ecosystem degradation and coastal resilience over time.

• Vantor is partnering with Google Earth AI to integrate more advanced geospatial reasoning capabilities into its Tensorglobe platform. The aim is to move beyond basic object detection towards understanding more complex spatial behaviour and relationships within imagery and geospatial data. One of the more notable aspects is that these capabilities are being designed for classified and air-gapped environments, where many large AI systems are still difficult to deploy securely.

• NOAA is using AI and cloud infrastructure to modernise weather forecasting workflows by migrating large volumes of historical climate and meteorological data into cloud-based systems. The broader goal is to speed up forecasting pipelines for extreme weather events and improve model efficiency, particularly as data volumes continue to grow. At the same time, NOAA officials have stressed that AI tools are intended to support rather than replace scientific expertise, especially for high-stakes forecasting decisions.

• Google Earth is expanding its professional geospatial toolkit by adding global data layers and native Earth Engine raster support directly into its no-code interface. The update also includes an AI-powered feature called “Ask Google Earth”, which allows users to search for and visualise datasets using natural language prompts. The broader direction is fairly clear: reducing the technical barriers required to work with large-scale geospatial datasets and remote sensing products.

Jobs

• TomTom is looking for a Staff UX Designer under their Map Visualisation team based in Amsterdam.

• Esri is looking for a remote GIS Project Manager under their Assessment Department based in Canada.

• Stantec is looking for a GIS Analyst under their Energy and Resources budiness unit based in Toronto.

• EIB is looking for a Data Warehouse Officer based in Luxembourg.

Just for Fun

USGS has brilliantly released a seamless, globally consistent geologic map of the entire Moon at an incredible 1:5,000,000 scale.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that’s slightly cheaper than your ESRI subscription. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Nightlights Harmonisation: Deep learning links DMSP and VIIRS.

• Map Navigation: AI struggles with route planning.

• Earth World Models: RS-WorldModel predicts future satellite scenes.

• Satellite Image Editing: RSEdit reshapes remote sensing imagery.

• Google Urban Embeddings: S2Vec summarises the built environment.

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Research you should know about

1. Harmonising nighttime light data across decades

Anyone who’s worked with nightlights data knows the long-running headache: DMSP-OLS data goes back to 1992 but suffers from saturation in city centres, blooming, and no on-board calibration. VIIRS, which kicked in from 2012, fixes most of these issues with finer resolution and a much wider dynamic range. The problem is, if you want a long time series stretching back before 2012, you’re stuck stitching together two sensors that don’t really speak the same language. A new paper addresses this with a deep learning approach that translates DMSP imagery into something that looks like it came out of VIIRS.

The authors use a CUT-based generator (a type of image-to-image translation model) trained on overlapping DMSP and VIIRS data. The idea is to teach the model what a VIIRS scene “should” look like given a DMSP input, so you can then run it on pre-2012 DMSP data and get a harmonised series. On their held-out test set of 1,862 patches, they get an R² of 0.87 on radiance, an RMSE of 0.114, and an SSIM (structural similarity) of 0.79. Visually, around 75% of cases look pretty clean, with the model successfully recovering internal structure in cities that DMSP had flattened into uniform blobs.

It’s not flawless though. About 8% of patches throw up artefacts, and the failure modes are pretty instructive. In megacity cores where DMSP is fully saturated at DN=63, the model can’t hallucinate plausible internal structure and produces checkerboard patterns from the upsampling kernel. Around isolated bright points like ports and gas flares, you get halo effects where the model over-generates radial glow. So if you’re using harmonised data for analysis of dense urban cores or industrial sites, you’ll want to flag those areas. For everything else, this looks like a useful step towards a properly continuous nightlights record from 1992 to today.

2. Testing if AI can navigate a map

Multimodal large language models (MLLMs) are becoming pretty smart at analysing satellite images. If you show them a photo of a landscape, they can usually identify the buildings, spot the rivers, and classify the terrain. But there’s a massive difference between simply recognising objects and actually planning a route through them. If a rescue team needs to know the safest path for an off-road vehicle across a flooded disaster zone, simply identifying the water isn’t enough; the AI needs to understand the vehicle’s capabilities, map out the terrain, and plot a strict, step-by-step route. Until now, there hasn’t been a reliable way to test if AI models can actually do this kind of complex, constraint-based planning.

A new study introduces NeSy-Route, a massive new testing framework designed to evaluate how well AI models can navigate remote sensing imagery. Instead of just asking the AI to pick the best route from a multiple-choice list, NeSy-Route forces the model to complete three increasingly difficult tasks. First, the AI has to read a text prompt (like “find the fastest route for a pedestrian avoiding water”) and translate those rules into strict mathematical constraints. Next, it has to look at the satellite image and correctly identify which parts of the terrain match those constraints. Finally, it has to generate an actual, coordinate-by-coordinate path from start to finish.

The researchers tested 10,821 different scenarios across a variety of top-tier AI models, and the results were pretty revealing. While most advanced models were pretty good at understanding the text constraints, they completely fell apart when asked to actually plot the route on the map. Even the smartest proprietary models generated highly inefficient paths, while open-source models often plotted routes that drove straight through restricted obstacles. Ultimately, this benchmark proves that while AI is great at describing satellite images, it still lacks the deep, global reasoning required to reliably navigate the real world. I don’t think this is surprising though, as LLMs are not trained/fine-tuned for the task (yet…).

3. A unified world model for Earth observation

If 2025 was the year of ‘foundation models’, 2026 is the year of ‘world models’. Basically, the idea is that a World Model understands complex environments and predict future states for things like autonomous driving and robotics. However, applying this concept to Earth observation has been pretty difficult. Typically, remote sensing algorithms are split into two completely separate camps. Models either understand and describe changes between satellite images, or they synthesise new landscapes from scratch. Because these tasks are kept isolated, the algorithms miss out on shared geographic knowledge, leaving generative models hard to control and analytical models unable to grasp future physical constraints.

RS-WorldModel is a new system that bridges this gap by tackling both spatiotemporal change analysis and future scene forecasting simultaneously in three stages. These stages are worth understanding because each does something distinct. First, Geo-Aware Generative Pre-training teaches the model to predict future scenes conditioned only on geospatial metadata, with no text involved. Second, Synergistic Instruction Tuning jointly trains the model on both change description and text-guided forecasting, so the two tasks reinforce each other. Third, Verifiable Reinforcement Optimization uses GRPO with task-specific rewards: cosine similarity for forecasting, and an LLM judge for change descriptions that can detect physically implausible outputs (e.g. impossible illumination changes given the sun angle). The ablations show each stage matters. Stripping out the geographic metadata alone bumps FID from 50.28 to 53.72, and removing the pre-training stage entirely sends FID up to 73.55.

The numbers are pretty compelling. On change description, RS-WorldModel beats Qwen3-VL-32B on most metrics despite being 16x smaller, with ROUGE-L jumping 21% over the same-size base model. On forecasting, it cuts FID by 48% relative to CRS-Diff and even beats Gemini-2.5-Flash Image (43.13 vs 46.14). The qualitative examples are particularly nice: when prompted with a winter scene description, the model preserves the underlying settlement layout while adding realistic snow cover and long shadows from a low sun angle, whereas the baselines either miss the structure or generate geographically implausible textures. For anyone working on land-use change, climate adaptation, or any application where you’d want to simulate future satellite imagery under different scenarios, this looks like a pretty interesting tool.

4. RSEdit: giving AI the power to reshape satellite imagery

While text-to-image AI models can easily generate highly realistic pictures of cats or fantasy landscapes, they struggle when asked to edit remote sensing satellite imagery. If you prompt a standard AI editor to show what a village would look like after a flood, it will often hallucinate buildings that don’t exist, distort the strict top-down perspective, or break the laws of physics. This happens because general-purpose AI models lack a deep understanding of geography and are built using conditioning methods that don’t align with the rigid spatial rules of Earth observation data.

To fix this, researchers built RSEdit, a new framework specifically designed to edit remote sensing imagery. Instead of forcing satellite data to fit a generic AI model, RSEdit adapts the underlying architecture of top-tier diffusion models (like Stable Diffusion and PixArt-α) to respect the strict spatial layout of satellite images. For older models, it tightly binds the original image to the edit instructions pixel-by-pixel, and for newer models, it treats the edit as a continuous stream of data tokens. The researchers trained the system on over 60,000 before-and-after satellite images detailing everything from urban expansion to natural disasters.

The results are pretty impressive. When asked to simulate complex scenarios like a tsunami hitting a coastline or a wildfire damaging specific structures, RSEdit generated physically plausible, highly accurate edits without hallucinating extra details or warping the surrounding roads and vegetation. In direct comparisons using specialised building-damage assessment tools, RSEdit outperformed leading general-domain editors like InstructPix2Pix by a massive margin. Ultimately, this framework provides a powerful, on-demand way to simulate disaster impacts and urban growth, creating a valuable new source of training data for environmental analysis.

Geospatial Datasets

1. High-res in-season crop mapping dataset

The newly developed In-season Crop-type Data Layer (ICDL) provides 10-metre resolution crop planting maps across the contiguous United States. You can access the data here and the code here.

2. Ancient China population dataset

A pretty original new dataset reconstructs county-level population distributions in ancient China for the pivotal years of 2, 742, 1102, and 1820 AD. It estimates population figures for over 6,000 counties across four representative dynasties. You can access the data here.

3. Off-shore marine cadastre dataset

The US Bureau of Ocean Energy Management has recently released a new Offshore Marine Cadastre Data Collection, essentially consolidating various regionally focused datasets into one streamlined national resource. You can access the data here or here. You can also check out the navigator here.

4. Comprehensive crop yield dataset

The CYCleSS dataset offers a large-scale resource for predicting crop yields across England, blending precision agriculture with environmental data. It features anonymised yield data from 934 fields alongside 10-km resolution satellite, daily weather, and soil type information from 2015 to 2017. You access the data here and the code here.

Other useful bits

• Google Research introduced S2Vec, a new AI framework that learns to read the built environment of our cities just like a human would. By transforming complex geospatial data into simple mathematical summaries, it provides a way of predicting important socioeconomic and environmental patterns across the globe.

• The WMO has published its latest State of the Global Climate report.

  It brings together evidence on ocean heat, sea ice, temperatures, greenhouse gases, and other indicators of a changing climate. It is also a useful reminder of why long-term Earth observation matters for climate monitoring, risk management, and public policy.

• The World Bank has launched the Land Data Map, an interactive platform for exploring land-related data. The tool makes it easier to see how land systems connect with development outcomes such as economic growth, jobs, and investment. It is a useful resource for anyone working on land governance, urban development, or spatial planning.

Jobs

• TomTom is looking for a Staff UX Designer under their Map Visualisation team based in Amsterdam.

• UN DESA is looking for two interns in (1) Geodesy & Geospatial Research Support and (2) Digital Design, Data Visualization & Geospatial Communications.

• Esri is looking for a remote GIS Project Manager under their Assessment Department based in Canada.

• Stantec is looking for a GIS Analyst under their Energy and Resources budiness unit based in Toronto.

Just for Fun

NASA has released some images of the SWOT satellite to map the Earth’s ocean floor.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — your weekly map to the latest geospatial news. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Urban Physics: PDEs model city sprawl dynamics

• Map Training: OpenStreetMap improves satellite AI captions

• EO Embeddings: Blueprint for stronger geospatial representations

• City Graphs: New Python library for urban networks

• Aerosol Forecasts: AI delivers rapid global predictions

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Research you should know about

1. Why do cities sprawl? The physics of urban growth

Between 1985 and 2015, the total amount of land covered by cities doubled, eating into green spaces, worsening air quality and making infrastructure incredibly expensive to run. Despite this massive transformation, actually explaining how and why cities expand the way they do is pretty difficult. Traditional urban economics often uses models that treat cities as if they are in a state of perfect balance, assuming that land is instantly redeveloped to its optimal use like a neat, circular dartboard. The reality is that cities grow in messy, uneven and unpredictable ways, driven by everything from traffic congestion to developer whims.

A new study argues that we should look at cities not through the lens of traditional economics, but through the physics of ‘surface growth’. In the same way that physicists use Partial Differential Equations (PDEs) to model the jagged edges of a growing bacterial colony or the spread of a tumour, these same mathematical tools can be used to model the irregular, sprawling boundaries of a city. Unlike older economic models, PDEs can handle the chaotic, real-world feedbacks of urban growth, such as how the construction of a new railway line suddenly makes a distant suburb boom, which in turn demands even more roads.

Recent studies have shown that this isn’t just an abstract theory. When researchers applied these physics-based PDE models to historical data from London and Sydney, they successfully replicated the real-world shift from dense, packed city cores to sprawling commuter belts over the last 150 years. The models naturally captured the ‘angiogenic’ growth of the cities, where population density and transport networks feed off each other in a constant cycle. Ultimately, the researchers argue that by merging complexity science with satellite data, planners can finally get a mathematical grip on urban sprawl, allowing them to test the long-term impact of new infrastructure before pouring any concrete.

2. Using maps to teach AI about satellite imagery

When it comes to processing satellite imagery, vision-language models (VLMs) have historically relied on a technique called ‘pseudo-labelling’ to learn about the Earth. Because high-quality, human-annotated satellite data is incredibly rare and expensive, developers usually rely on massive, general-purpose frontier models like GPT-5 or Gemini to act as a ‘teacher’. These teacher models look at satellite images and generate captions, which are then used to train a smaller ‘student’ model. However, this approach is both expensive and fundamentally flawed. The student model can never be smarter than its teacher, meaning it inherits all of the teacher’s mistakes and hallucinations.

Researchers have introduced OSMDA (OpenStreetMap-based Domain Adaptation) to break this reliance on expensive external teachers. The core idea is pretty simple: instead of asking a massive AI to guess what is in a satellite photo, the researchers pair the satellite image with a rendered map tile from OpenStreetMap. Because modern VLMs are already excellent at reading charts and OCR, the model can simply read the map to identify the roads, buildings and land-use polygons, and then cross-reference this with the satellite image to write a highly accurate, geographically grounded caption. This allows a capable base model to act as its own annotation engine.

Using this self-contained pipeline, the team generated OSMDA-Captions, a massive dataset of over 200,000 highly detailed image-caption pairs, entirely for free. When they mixed this new dataset with existing remote sensing data to train their OSMDA-VLM model, it achieved state-of-the-art results across the majority of ten rigorous benchmarks. It completely outperformed models that were trained using expensive frontier teachers, proving that leveraging crowdsourced geographic data is a far more scalable and accurate way to adapt AI to the remote sensing domain.

3. The blueprint for EO embeddings

Earth observation satellites generate massive amounts of data every day. To make analyzing this imagery more efficient, developers use Geospatial Foundation Models to compress raw satellite images into compact mathematical representations called embeddings. This is something I’ve covered previously here. By reusing these embeddings, pipelines can run much faster without constantly reprocessing the raw images. However, the best way to extract and design these embeddings (e.g. choosing the right model architecture, the best layers, or the optimal pooling method) has remained unclear, which can lead to suboptimal performance in real-world applications.

A recent study introduces a systematic blueprint for designing these embeddings by rigorously evaluating various GeoFM configurations using the NeuCo-Bench framework. The research team tested how different architectural decisions, pretraining strategies, and data aggregation methods impact the AI’s ability to track diverse environmental factors, from crop coverage to biomass and urban heat islands.

The findings offer some useful guidelines for developers. The study shows that Vision Transformers consistently produce stronger, more versatile embeddings than Convolutional Neural Networks (CNNs) for these tasks. When compressing the spatial data, simple “mean pooling” (basically averaging out the features) proved to be the best strategy across the board (simple is best as they say…). Interestingly, the researchers discovered that pulling data from the final layer of a CNN often degrades accuracy for tracking continuous physical variables, meaning developers should extract data from intermediate layers instead. Finally, the study highlights that stitching together embeddings from models trained with different self-supervised learning objectives creates highly robust representations, effectively compressing satellite data by a factor of 500 while maintaining exceptional predictive power.

Geospatial Datasets

1. Cactus ecological and evolutionary dataset

CactEcoDB brings together spatial, environmental, trait, and phylogenetic data for over 1,000 cactus species. It combines occurrence records with a time-calibrated phylogeny, making it useful for studying biodiversity patterns, species evolution, and conservation risks. You can access the data here and the code here.

2. Pore-scale CO2-water interaction dataset

This new dataset captures how CO2 and water interact within porous media at the pore scale, with relevance for geoscience and carbon capture and storage. It includes 624 high-resolution 2D samples across 100 time steps under a constant injection rate, generated using numerical simulations. You can access the files here and the code here.

3. High-precision landslide event dataset

This dataset maps 1,582 landslide events across mainland China between 2008 and 2024. It uses LLMs to extract information from local news reports, capturing detailed timing and location data, often at village level. You can access the data here and the code here.

4. Forest disturbance deep learning dataset

The Deep4Dist dataset provides around 17,500 high-resolution image patches (0.2 m) from Rhineland-Palatinate, Germany. It combines RGB, near-infrared, and height data, with segmentation labels for disturbance types such as bark beetle damage, clear cuts, and windthrow. Access the data here and the code here.

Other useful bits

• A new Python library called City2Graph has just launched, allowing developers to transform complex urban datasets into seamless graph representations. It’s a pretty useful resource for anyone looking to build advanced spatial network analyses or power innovative artificial intelligence applications using city mobility and infrastructure data.

• The Planet and NVIDIA are teaming up to build a new AI-powered engine that processes planetary satellite imagery in seconds rather than hours. The system uses high-performance computing to generate higher-resolution outputs and enables a searchable, near real-time view of the Earth. This improves access to up-to-date geospatial data for monitoring and analysis.

• SpaceX has exceeded 10,000 active Starlink satellites in low Earth orbit following a double launch. This marks a significant expansion of its global broadband network, increasing coverage and capacity across regions.

• Researchers in China have introduced an AI-based system for global aerosol and weather forecasting. Trained on over 40 years of data, it produces detailed five-day forecasts in under a minute. The model supports faster environmental monitoring and improved understanding of atmospheric conditions.

Jobs

• UN DESA is looking for two interns in (1) Geodesy & Geospatial Research Support and (2) Digital Design, Data Visualization & Geospatial Communications.

• UNOPS is looking for an ICT Associate based in Valencia.

• Esri is looking for a remote GIS Project Manager under their Assessment Department based in Canada.

• Stantec is looking for a GIS Analyst under their Energy and Resources budiness unit based in Toronto.

Just for Fun

In celebration of Earth Day, NASA has released a cool interactive tool that spells out any name using natural geographic features captured by Landsat satellites.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge. If you called your high school cover band Rasterheads, we have one thing to say to you. Welcome home… The aim of this newsletter is, as always, to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Map Compression: Optimising tiles without losing spatial detail

• Embedding Methods: Moving beyond averages improves model accuracy

• LLM Reliability: Reducing hallucinations in satellite image analysis

• 5G Prediction: Fixing spatial bias in network demand models

• Soil Dataset: High-resolution global soil carbon mapping

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Research you should know about

1. Shrinking massive map tiles without losing the details

If you’re reading this newsletter, chances are you love a good interactive map. And let’s face it, who doesn’t? Well, making them fast and customisable is actually pretty difficult. Right now, developers are forced to choose between two flawed options. Server-side rendering generates static image tiles that load quickly, but they lock users into a single visual style with no room for client-side tweaking. Conversely, client-side rendering sends raw vector data to the browser for fully flexible styling, but these vector files don’t have size limits. As datasets grow, vector tiles become bloated with overlapping geometries and endless attribute tables, which chokes network bandwidth and browser memory. Existing compression tools try to fix this by blindly dropping data, but this typically warps geographical boundaries and deletes the exact attributes users want to visualise. Not great…

A new study introduces HiFIVE, a framework that treats map tile reduction as a strict mathematical optimisation problem. Instead of indiscriminately deleting polygons, the system uses a two-stage approach to shrink tiles to a strict size limit while preserving their visual and semantic fidelity. First, a lightweight triage step makes rough cuts by discarding visually insignificant records. Next, a highly precise ‘sparsification’ algorithm steps in. This algorithm weighs the visual prominence of a shape (such as its pixel footprint) against the usefulness of its underlying data. It selectively nullifies specific attribute values that are unlikely to be used for styling, like random unique identifiers, while preserving categorical data like state names or road types.

When tested on massive terabyte-scale datasets, HiFIVE processed almost a billion records in under an hour. The resulting maps proved to be vastly superior to those produced by standard reduction tools. By intelligently choosing exactly which data points to keep and which to discard, the framework maintained crisp, undistorted boundaries and retained the necessary attributes for complex client-side styling. Ultimately, this approach provides a highly scalable way to explore massive spatial datasets directly in the browser without sacrificing rendering speed or visual accuracy.

You can access the code here.

2. Why averaging satellite data is a bad idea

Geospatial foundation models are brilliant at turning satellite images into highly detailed pixel-level data. However, many practical applications require labelling entire regions, such as a whole farm or a residential neighbourhood. To make this work, developers typically compress thousands of pixel vectors into a single representation using a technique called mean pooling, which is essentially just averaging everything out. The major flaw with this default approach is that it completely erases all the nuanced variations within a patch. When these models are deployed in new geographic areas, this loss of detail causes their accuracy to drop significantly.

A team of researchers built a new dataset called EuroSAT-Embed. They generated 81,000 embedding files using three different foundation models and benchmarked eleven training-free pooling methods. Instead of just averaging the pixels, they tested richer statistical summaries that capture the extremes and the variability within a specific region. For example, a method called stats pooling combines the minimum, maximum, average and standard deviation of the pixels, while covariance pooling looks at the complex relationships between all the different data points.

The results clearly show that moving beyond simple averages makes a massive difference for spatial generalisation. By using richer pooling schemes, the researchers managed to cut the geographic accuracy gap in half and boosted overall performance by up to 6 per cent on spatial splits. They recommend a straightforward three-tier strategy for anyone working with earth embeddings. Mean pooling is fine for a quick baseline, but stats pooling should absolutely be the default choice, while covariance pooling should be reserved for projects where peak accuracy is the absolute priority.

3. Stopping AI from hallucinating over satellite maps

Multimodal LLMs are great at many things, but they tend to make things up when looking at satellite imagery. This issue, known as hallucination, usually happens for two reasons. Either the models get distracted by the massive scale of remote sensing images and can’t find the right area, or the targets are simply too small and cluttered to see clearly. To figure out exactly what is going wrong, researchers built RSHBench, a new diagnostic tool. It proved that models mostly suffer from factual errors, like making up objects or misidentifying attributes, which then snowball into flawed logic.

A clever training-free framework called RADAR essentially forces the AI to use its own internal attention mechanisms to zoom in on the image in two distinct stages. First, it asks a location-oriented query to pinpoint exactly where the relevant visual evidence is hiding. Once it locks onto that specific region, it uses a content-oriented query to zoom in and extract the fine-grained details needed to answer the prompt accurately.

When put to the test across a variety of major open-source and proprietary models, the results were highly impressive. By forcing the AI to actively seek out and refine visual evidence before making a guess, the RADAR framework consistently boosted overall accuracy by 2 to 4 per cent. More importantly, it slashed both factual and logical hallucination rates by around 10 per cent. Ultimately, this gives developers a highly scalable, plug-and-play way to make foundation models much more reliable for analysing complex Earth observation data.

4. Smarter spatial predictions for 5G networks

Predicting exactly where mobile network traffic will spike is essential for planning 5G and 6G infrastructure. While machine learning is great at combining different types of data (like population density and local land use) to map out demand, it often falls into a trap called spatial autocorrelation. Because neighbouring city blocks tend to have very similar traffic patterns, standard AI training methods accidentally leak information between the training and testing datasets. As a result, network planners get a model that looks highly accurate on paper but fails to generalise when deployed in the real world, leading to poorly placed cell towers and wasted spectrum.

A team of researchers developed a new AI-driven framework that forces the model to actually learn rather than just memorise nearby locations. They introduced a two-stage splitting strategy. First, it divides the city into geographically separate clusters to cut off the spatial data leakage. Then, it refines these clusters based on local context, such as whether an area is residential or commercial, to make sure the AI is tested on a truly diverse set of environments. Finally, they apply a Spatial Error Model to scrub out any leftover geographical biases from the predictions.

They tested the framework across five major Canadian cities using a massive crowdsourced dataset of 15 million mobile usage measurements. The new method consistently slashed prediction errors compared to basic location clustering, giving engineers a much more reliable picture of true network demand. When applied to real-world scenarios like 5G spectrum planning, these tighter predictions successfully prevented the costly overestimation or underestimation of network congestion. Ultimately, this gives telecom operators a much sharper tool for figuring out exactly where to deploy their next wave of antennas.

Geospatial Datasets

1. Global soil carbon dynamics dataset

The OpenLandMap-soildb dataset provides the most detailed information to date on global soil carbon dynamics. It integrates over 600,000 field measurements with satellite data and machine learning, and maps soil organic carbon density and content at a 30-metre resolution across three soil depths from the year 2000 onwards. You can access the maps here, the files here, and the code here.

2. Inland water pH and alkalinity dataset

The newly compiled PHALK dataset provides a global assessment of pH and alkalinity across the world’s inland surface waters. It collates chemical data from 18 databases and 55 scientific publications, and maps over 1 million drainage basins. You can access the data here and the code here.

3. Global ocean boundary current dataset

GOXBT dataset provides high-resolution data on the dynamics of major ocean boundary currents. You can access the data here and the code here.

Other useful bits

• ESA’s CryoSat ice-monitoring satellite has received a new software upgrade that allows it to now measure Earth’s magnetic field. Originally designed simply to keep the spacecraft on track, its onboard magnetometer recently captured incredibly precise data during a massive solar storm, offering researchers a fantastic, unexpected bonus alongside dedicated magnetic missions.

• 25 new Open Buildings datasets are now available on HDX thanks to the team at Google Research.

  

• By measuring microscopic changes in gravity on the ocean's surface, a new satellite has helped researchers create the most accurate map of the seafloor to date. One finding is that small underwater hills cover a 70% of the ocean floor 🤯.

• Satellogic has just announced Merlin, a new satellite constellation designed to map the planet every day at 1m resolution. It’s expected to become operational by first half of 2027.

Jobs

• UN DESA is looking for two interns in (1) Geodesy & Geospatial Research Support and (2) Digital Design, Data Visualization & Geospatial Communications.

• UNOPS is looking for an ICT Associate based in Valencia.

• FAO is looking for a Remote Sensing and GIS Specialist based in Rome.

• Esri is looking for a remote GIS Project Manager under their Assessment Department based in Canada.

• ERM is looking for a Spatial Analyst (Remote Sensing) based in Australia.

Just for Fun

The recent equinox saw the sun align perfectly over the equator, treating everyone across the globe to an equal measure of day and night as spring arrives in the north and autumn settles in the south.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that has more rhythm than dembow… In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Radar Model: Billion-scale AI improves SAR image understanding

• Water Resilience: New index captures true water scarcity risks

• Cloud-Free Imaging: AI merges cloud removal and sharpening

• Canopy Heights: Global 1m forest height dataset from Meta and WRI released

• Cropland Emissions: New dataset maps global agricultural emissions

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Research you should know about

1. A billion-scale model for understanding radar imagery

As long time readers know, Synthetic Aperture Radar (SAR) is an incredibly powerful tool for earth observation because it can see through clouds and operate in the pitch black. But teaching AI to understand radar images can be pretty difficult. Unlike normal optical photos, SAR images are plagued by granular “speckle” noise and severe geometric distortions caused by the radar’s side-looking angle. Even worse, SAR data is pretty fragmented. A model trained to identify buildings using one satellite’s specific frequency band or polarisation mode will often completely fail when looking at data from a different satellite. This extreme “domain gap” has prevented the creation of a universal AI model for radar imagery.

Researchers have built CrossEarth-SAR to solve this, the first billion-scale vision foundation model specifically designed to handle the massive diversity of radar data. To keep computational costs down while still using billions of parameters, they employed a “sparse Mixture-of-Experts” (MoE) architecture. Instead of every part of the neural network working on every image, the model uses a “router” to send specific types of radar images to specialised “expert” sub-networks. As part of this, the team built a novel “physics-guided” router that uses actual radar physics (e.g. speckle strength and directional entropy) to decide which expert should handle which image, ensuring the AI adapts to the specific characteristics of the satellite that took the picture.

To train this massive model, the team assembled a dataset of 200,000 radar images, using existing AI models to automatically generate “pseudo-labels” for unlabelled data. They then tested CrossEarth-SAR across 22 benchmarks designed to simulate real-world domain gaps. The model achieved state-of-the-art results on 20 of the 22 benchmarks, outperforming previous models by over 10 per cent in some of the hardest multi-gap scenarios. This suggests that by explicitly hardwiring radar physics into the AI’s routing mechanism, it’s possible to build a single, robust model capable of interpreting radar data from almost any satellite on Earth.

You can check out the project page here and the code here.

2. A smarter way to measure water scarcity

When we think of water scarcity, we usually just look at how much rain is falling or how full the reservoirs are. But a new paper from Scientific Reports argues that this is missing the bigger picture. Whether a community actually runs out of water has just as much to do with politics, infrastructure and economics as it does with hydrology. To fix this, researchers have proposed the Integrated Water Strategic Resilience Index (IWSRI). Instead of just measuring physical water, this new index acts as a broader, more holistic dashboard, combining data on climate variability, governance quality, political stability and economic adaptability to reveal a country’s true ‘water resilience’.

To test this new tool, the team applied it to the Middle East and North Africa, which is essentially ground zero for water stress. The results challenge some common assumptions. For example, Israel, Qatar and the UAE are practically deserts, yet they scored incredibly high on the resilience index. Why? Because at the time they had wealthy, stable governments that had invested heavily in desalination and wastewater recycling. They might be regretting that rating now… Anyway, on the flip side, countries like Yemen, Syria and Libya scored at the absolute bottom. It’s not just that they lack water; it’s that years of war, political instability and destroyed infrastructure have completely crippled their ability to manage the resources they do have.

The authors point out that traditional metrics like the Falkenmark Indicator would completely miss these nuances by focusing solely on per-capita water availability. By integrating sociopolitical factors, the IWSRI shows that water scarcity is often a manufactured crisis. While the researchers acknowledge that quantifying things like ‘governance’ can be tricky, this new index provides a much more realistic map of where the world’s water systems are actually breaking down.

3. Seeing through the clouds with a new all-in-one AI

Satellites monitoring the Earth face a frustrating compromise between detail and frequency. You can either get highly detailed images every few weeks or blurry images every few days. To make matters worse, optical sensors are frequently blocked by clouds, resulting in massive gaps in data. Until now, scientists have tried to fix this by using artificial intelligence to first remove the clouds and then artificially sharpen the images through a process called spatiotemporal fusion. However, treating these as two completely separate tasks often introduces compounding errors that ruin the final picture.

This is what SuperSTF tries to address. It’s essentially a framework that tackles cloud removal and image sharpening at the exact same time. Instead of operating directly on the raw pixels, the system uses a latent diffusion model paired with a Swin Transformer. In simple terms, it compresses the images into a more efficient hidden mathematical space, fuses the frequent blurry data with the rare sharp data, and then rebuilds a pristine image. The model is also fed specific data about cloud locations and the time of year to help it intelligently guess what is hiding beneath the cloud cover.

When tested across diverse regions like urban Washington and agricultural Texas, SuperSTF completely outperformed existing sequential methods. It successfully reconstructed sharp, cloud-free image sequences even when up to 70 per cent of the original high-resolution reference image was obscured by thick clouds. Because it operates in a compressed latent space rather than processing every single pixel during its diffusion phase, it’s also surprisingly fast and computationally efficient. Ultimately, this gives scientists a much more reliable tool for tracking rapid environmental changes, crop health, and urban development without having to constantly wait for a clear sky.

Geospatial Datasets

1. Meta & WRI’s high-res global canopy height map

The newly updated CHMv2 dataset, created by a collaboration between the World Resources Institute and Meta, provides a 1-metre resolution global canopy height map. You can access the data here and the code here.

2. Global cropland emissions dataset

A new dataset from Cornell University and the Land & Carbon Lab maps the greenhouse gas emissions generated by the world's croplands (rice, maize, wheat, and oil palm). You can access the data and code here.

3. Convective weather forecasting dataset

The newly introduced CIDS dataset is designed to train machine learning models for detecting and nowcasting severe convective weather. It covers southeastern China from 2018 to 2023, and captures intense events like short-duration heavy rainfall, thunderstorm winds, and hail at crisp 10-minute intervals. You can access the data here and the code here.

4. Global cropland and irrigation dataset

The MIRCA-OS dataset maps monthly irrigated and rainfed harvested areas between 2000 and 2015. It includes 5-arcminute gridded data for 23 major crop classes. You can access the data here and the code here.

Other useful bits

• ESA’s EarthCARE satellite has just delivered some new global maps of atmospheric dust trails. By using an advanced laser instrument to track how these tiny mineral particles travel across the globe, researchers are getting a wonderfully clear picture of how dust impacts our climate and weather systems.

• Google Earth’s 2026 roadmap has been unveiled, showcasing a shift towards professional workflows. We’re going to see everything from global elevation contours and seamless custom data imports to AI assistants that turn natural language into instant geospatial insights.

• A new initiative in Australia is using Google Earth AI and a bunch of spatial datasets to tackle heart disease in remote communities. By combining environmental data with health records, the approach identifies spatial patterns in risk exposure. The aim is to support earlier intervention by directing care to locations where underlying risk factors are highest.

Jobs

• The UN Department of Economic and Social Affairs is looking for two interns in (1) Geodesy & Geospatial Research Support and (2) Digital Design, Data Visualization & Geospatial Communications.

• FAO is looking for a Remote Sensing and GIS Specialist based in Rome.

• IOM is looking for a Displacement Tracking Matrix (DTM) Systems Developer and Architect based in Geneva.

• UNGSC is looking for a remote ArcGIS Enterprise Administrator from Europe, Middle East and North Africa.

• GHD is looking for a GIS Analyst based in Melbourne.

Just for Fun

This is pretty nuts… 143 million people who spent the last eight years catching Pokémon were actually building one of the largest real-world visual datasets in AI history. Niantic has just revealed that this staggering collection of over 30 billion crowdsourced images is now being cleverly used to train visual navigation AI for delivery robots.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that’s as frequent as a Marvel movie. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Spatial AI: LLMs struggle with pure GPS math.

• Change Detection: Foundation model tracks complex landscape changes.

• War Mapping: Satellites track destruction in conflict zones.

• Urban Shade: High-resolution dataset maps global pedestrian shade.

• Image Alignment: Massive dataset of aligned satellite pairs.

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Research you should know about

1. Do AI models actually understand GPS coordinates?

Large Language Models (LLMs) are increasingly being built into navigation apps and geographical tools, but their ability to actually understand and manipulate GPS coordinates has remained a bit of a mystery. Most spatial AI tests involve asking a model to describe where an object is in a photo of a living room, which doesn’t really translate to calculating the distance between Tokyo and Sydney. To bridge this gap, researchers built GPSBENCH, a new dataset designed to test whether LLMs genuinely understand the Earth’s coordinate system or if they are just bluffing.

The researchers tested 14 top-tier LLMs across two main tracks: ‘Pure GPS’ (geometric math like calculating distances or bearings) and ‘Applied’ (using world knowledge, like identifying which city sits at a specific coordinate). The results show a pretty stark divide. Most models are actually much better at the ‘Applied’ track, using their vast pre-trained world knowledge to guess locations, but they completely fall apart when asked to do the pure spherical geometry required for the ‘Pure GPS’ track. For instance, tasks like calculating the area of a polygon on the globe had a near 0 per cent success rate across the board, proving that the models haven’t actually learned geodetic formulas.

Interestingly, the study also revealed that LLMs have a highly hierarchical understanding of geography. They can almost always identify the correct country for a given GPS coordinate, but their accuracy drops off a cliff when asked to pinpoint the specific city. However, when the researchers added random ‘noise’ to the coordinates, the models’ performance didn’t drop, which proves they aren’t just memorising coordinates from databases but do have a genuine, albeit fuzzy, spatial understanding. Ultimately, this shows that if you want an AI to perform precise geographic reasoning, you still need to augment it with external calculators or specific GPS tools, because right now, their internal compass is a bit broken.

You can access the data and code here.

2. A new foundation model approach to tracking landscape changes

Tracking changes on the Earth’s surface usually relies on binary change detection, which simply spots where a landscape has been altered. Semantic change detection takes this a step further by identifying exactly what those changes are, such as a dense forest being replaced by a paved car park. While incredibly useful for urban planning and disaster relief, building models for this task is notoriously difficult. Most current systems rely on standard visual encoders trained on everyday photos, leaving them poorly equipped to understand the complex, top-down perspective of satellite imagery. Furthermore, because the vast majority of land remains unchanged between satellite passes, the models often get overwhelmed by the sheer imbalance of data and resort to lazy predictions.

A new paper has introduced PerASCD, a new framework driven by a massive remote sensing foundation model. By leveraging an AI that has been explicitly pre-trained on vast amounts of satellite data, the system possesses a much deeper, native understanding of geospatial features. The team paired this foundation model with a custom-built Cascaded Gated Decoder. Older methods typically forced the AI through highly convoluted, multi-branch pathways to separate the before and after semantics. This new decoder streamlines the entire process, handling feature compression, multi-level fusion, and upsampling in a single modular pipeline, which drastically simplifies the overall architecture.

The researchers also patched a frustrating mathematical glitch that has plagued previous models. Older systems used a rigid loss function to force the AI to separate changed and unchanged pixels, but the harsh mathematical boundaries of this rule would often cause the model’s accuracy to suddenly collapse during training. By introducing a softened version of this mathematical rule, the team smoothed out the learning gradients and eliminated the numerical instability. When tested on major benchmark datasets like SECOND and LandsatSCD, the framework achieved state-of-the-art performance, delivering significantly sharper boundaries and fewer false positives than previous methods.

3. Mapping war zones with combined satellite data

The ongoing war in Ukraine has caused immense damage to infrastructure, agriculture and the broader environment. Obviously, sending survey teams into active conflict zones to assess this wreckage is incredibly dangerous and often impossible. To get around this, a new study from Scientific Reports have developed a new method to automatically map the destruction from space using freely available satellite data. By combining radar data from Sentinel-1 with optical imagery from Sentinel-2, they have created a system that monitors conflict-related changes without putting anyone in harm’s way.

The interesting part of this approach is how it adapts to different types of terrain. The algorithm automatically classifies the land cover (separating urban environments from rural or agricultural areas) and then chooses the best analysis strategy for that specific zone. The researchers combined radar-based change detection with optical image classification to spot destruction that single-sensor methods might miss. They also added a ‘context-aware smoothing’ technique, which essentially filters out visual noise and local glitches to produce a much cleaner and more reliable map.

When put to the test, the results were pretty impressive. The team compared their automated findings against the official UNOSAT database and found that their tool successfully detected over 80 per cent of damaged buildings. Furthermore, their smoothing technique proved to be far more accurate at detecting built-up areas than existing global land-cover products like AlphaEarth. Ultimately, this gives humanitarian organisations a rapid and reliable way to pinpoint destruction in restricted regions.

You can access the data here and the code here.

Geospatial Datasets

1. Urban pedestrian shade mapping dataset

This dataset provides high-resolution simulations of pedestrian shade provision across nine geographically diverse global cities, including Amsterdam, Sydney, and Rio de Janeiro. You can access the data here and the code here.

2. SAR-Optical image alignment dataset

The SOMA-1M dataset offers a massive collection of over 1.3 million precisely aligned Synthetic Aperture Radar (SAR) and optical image pairs. The dataset will be publicly released here.

3. Multimodal environmental monitoring dataset

The newly introduced MMEARTH-BENCH is a collection of five environmental monitoring tasks designed to evaluate geospatial machine learning models on a global scale. It features 12 diverse data modalities and includes geographic test splits to specifically challenge how well algorithms adapt to unseen regions. You can access the dataset, code, and read more about the project here.

4. Global subnational farm size dataset

This dataset provides the most comprehensive and spatially detailed empirical assessment of global mean farm size to date, targeting the year 2000. Unlike previous products that rely on national averages or modelled data, this collection compiles census and survey information at the smallest available subnational administrative level for 101 countries, alongside national data for 99 others. You can access the data and code here.

Other useful bits

• The team behind AlphaEarth Foundations has just rolled out their 2025 Satellite Embedding dataset, cleverly packing an entire year's worth of satellite data into neat 10-metre pixels. This update makes it easier than ever to spot global shifts without the headache of processing raw imagery.

• Google DeepMind has showcased a new realistic city planner app powered by Gemini 3.1 Pro. It tackles complex terrain and simulates traffic to generate incredibly high-quality visualisations of future urban infrastructure.

• A new time series foundation model called TimesFM has just been released, which has been pre-trained with 100 billion data points. It’s showing pretty impressive performance across a whole host of domains, making it a useful resource for anyone keen to dive into machine learning and forecasting.

Jobs

• Mapbox is looking for a remote Software Development Engineer based in either Germany or Finland.

• The European Centre for Medium-Range Weather Forecasts (ECMWF) is looking for a (1) Data Management Engineer and (2) Climate Data and AI/Machine Learning Scientist, both based in either Bonn, Germany or Reading, UK.

• The European Organisation for Nuclear Research (CERN) is looking for a GIS Specialist and Developer based in Geneva.

Just for Fun

The Artemis II crew ran into a very Earthly problem whilst en route to the Moon. Just hours into their ten-day flyby, the astronauts had to radio mission control for IT support after their Microsoft Outlook crashed. It’s comforting to know that no one is safe from an email glitch.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a safe space for Terra and exactextract enthusiasts... In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Human Development: Satellites map local development levels.

• Flood Prediction: LLMs estimate damage without training.

• Ecological Mapping: Testing foundation models on ecosystems.

• Urban Inequality: Satellites reveal China’s built disparities.

• Hyperspectral Data: Planet expands its open dataset.

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Research you should know about

1. A much better way to map human development

For years, the UN’s Human Development Index (HDI) has been the gold standard for measuring a country’s wellbeing, factoring in health, education and income. The problem is that HDI data is generally only available at the national or, at best, provincial level. This makes it almost useless for local policy decisions, like deciding which specific neighbourhoods need a new clinic or school. A new paper from Nature Communications used satellite imagery and machine learning to produce the first high-resolution global map of HDI, estimating development levels for over 60,000 municipalities and a 10km-by-10km grid worldwide.

The team achieved this using an approach called MOSAIKS, which translates satellite imagery into a set of random convolutional features. Rather than trying to map irregularly shaped provinces directly, the model learns the relationship between the visual features of an area (like building density, road networks and nighttime lights) and its known HDI. Because this relationship is linear, they can train the model on coarse provincial data and then “downscale” it to predict HDI for much smaller areas based purely on what the satellites see. They validated this by testing it against ground-truth municipal census data in Mexico, Brazil and Indonesia, proving the model could accurately spot local variations.

The results expose a massive flaw in how we currently look at development. The researchers found that if you only use provincial data, more than half of the global population is assigned to the wrong HDI quintile within their country due to “aggregation bias”. It essentially lumps rich urban centres and poor rural outskirts into a single misleading average. By contrast, this new high-resolution data allows policymakers to target aid far more accurately. In a simulated test in Mexico, using municipal data rather than provincial data increased the accuracy of targeting aid to the poorest 10 per cent of the population by over 11 percentage points.

You can access the code here.

2. Predicting flood damage without training models from scratch

Predicting property damage immediately after a flood usually involves training complex machine learning models on highly specific regional data. This requires an immense amount of upstream effort to assemble datasets, engineer features and handle skewed data where extreme damage cases are rare. To bypass this bottleneck, a new study has introduced R2RAG-Flood, a new framework that uses LLMs to predict post-storm Property Damage Extent (PDE) without requiring any task-specific training or fine-tuning.

Instead of fitting new parameters, the framework uses Retrieval-Augmented Generation (RAG). It takes historical tabular data about flood events (like rainfall, elevation and building density) and converts it into concise text summaries paired with logical “reasoning trajectories.” When a new storm hits, the system looks at a specific target area and searches its knowledge base for similar historical cases nearby (geospatial neighbours) and typical examples of different damage levels (prototypes). It feeds these examples to an LLM, asking it to emulate the reasoning process to classify the new target into low, medium or high damage.

The team tested this approach on data from Hurricane Harvey in Harris County, Texas. While a traditional supervised model achieved 0.714 overall accuracy, the training-free R2RAG-Flood approach was highly competitive, hitting up to 0.668 accuracy depending on the LLM backbone used. More importantly, the RAG approach outputs a structured, human-readable rationale explaining exactly why it predicted a certain level of damage. When factoring in the cost of API calls versus the computing power needed to train a model from scratch, the lightweight LLM variants proved significantly more cost-efficient, offering a scalable way to rapidly assess flood impacts in new regions without heavy computational overhead.

3. Putting AI foundation models to the ultimate ecological test

Geospatial Foundation Models (GFMs) are, of course, all the rage for earth observation, but mapping complex ecosystems is a whole different beast compared to drawing basic land cover maps. To see if these models actually live up to the hype, researchers put two leading GFMs (Prithvi-EO-2.0 and TerraMind) to the test against a standard deep learning baseline. They wanted to find out if these systems could handle highly specialised jobs like estimating forest canopy density across the United States and tracking the extent of carbon-rich peatlands in Patagonia.

The results were pretty clear: both GFMs comfortably beat the standard baseline model across all the ecological tasks. By being pre-trained on massive amounts of satellite data, these models avoided the usual pitfalls of standard image classifiers and adapted much better to geospatial nuances. TerraMind slightly outperformed Prithvi on basic tasks, but it really pulled ahead when the researchers fed it multimodal inputs. By combining standard optical imagery with radar and elevation data, the model became much better at separating tricky vegetation types, like distinguishing vital peat moss from generic grass.

Despite the impressive performance, the study highlighted some pretty big roadblocks before we can completely hand ecological mapping over to AI. For one, these models are highly sensitive to the quality of the training labels. When the researchers used coarse, off-the-shelf datasets to teach the models, the AI struggled to draw accurate boundaries, proving that you still need high-fidelity, expert-verified field data. Furthermore, the standard 10-metre resolution of satellite inputs proved to be a major limitation for capturing fine-scale ecological changes. So while these foundation models are a massive leap forward, they still need sharper data and better ground truths to truly map out the complexities of the natural world.

4. Satellite mapping reveals deep built environment disparities in urban China

China’s cities have grown at a breakneck pace over the last few decades, but figuring out exactly how equitable this growth has been at the street level is notoriously difficult due to a lack of building-level data. To tackle this, a team of researchers just released SinoBF-1, a massive national map that categorises the specific function of 110 million individual buildings across 109 major Chinese cities. By combining 1-metre high-resolution satellite imagery with other multi-modal data, they essentially built a microscopic view of the country’s urban landscape to track exactly where resources are concentrated.

Using nine distinct indicators covering everything from facility accessibility to infrastructure sufficiency, the study reveals some pretty big disparities across different regions and city tiers. Unsurprisingly, top-tier megacities dominate when it comes to amenity diversity and access to services, with these perks dropping off a cliff as you move down to lower-tier cities. However, the data shows that mid-tier cities actually offer the most equitable housing allocation. Geographically, southern cities boast the best access to healthcare and education, but this comes at the cost of severe infrastructure overcrowding compared to other regions.

Zooming in on individual cities, the map also highlights a growing divide between old and new neighbourhoods. The researchers found that newer, recently expanded zones on the outskirts of cities suffer from much greater built environment disparities than the early-established urban cores. This spatial footprint serves as a physical ledger of national development policies over the past half century, offering a highly detailed framework for tracking urban inequality in rapidly developing areas.

Access the data here and the code here.

Geospatial Datasets

1. Open hyperspectral satellite dataset

Planet has recently expanded its Open STAC Catalog with nearly 100 new hyperspectral scenes, offering an impressive global footprint for geospatial exploration. You can access the data here.

2. Tile-drainage and Rotation-Enhanced Cropland (TREC) dataset

The newly developed Tile-drainage and Rotation-Enhanced Cropland (TREC) dataset significantly improves the spatial accuracy of ecohydrological models across the Conterminous United States. You can access the data here and the code here.

3. High-resolution global precipitation dataset

The newly updated PERSIANN-CCS-CDR Version 2.0 offers a remarkable high-resolution global precipitation climate record, boasting a 0.04° spatial and 3-hourly temporal resolution. You can access the data here and the code here.

4. Global water storage anomaly dataset

The newly released ML-TWiX dataset is a brilliant resource that extends our understanding of global total water storage anomalies back to 1980, well before the launch of the GRACE satellite missions. You can access the data and code here.

Other useful bits

• Vantor has cleverly teamed up with Google to integrate powerful Earth AI imagery models into its Tensorglobe platform, allowing these tools to be used in secure, air-gapped environments for the very first time. By combining cutting-edge AI with a massive archive of high-resolution satellite imagery, it offers a brilliant new way to effortlessly monitor infrastructure and track changes offline.

• To tackle AI's struggle with 'spatial grammar', researchers have built MapTrace, a new pipeline that generates millions of synthetic map-path pairs for training. This approach has already given models a massive boost in accurately tracing paths on real-world maps, proving just how useful synthetic data can be.

• A developer has cleverly forked Apple’s Embedding Atlas to create Geospatial Atlas, a new open-source tool for exploring massive datasets of up to 100 million points right in your web browser. It brings together tools like WebGPU, DuckDB, and MapLibre. This essentially provides a smooth and interactive way to visualise huge amounts of spatial data without needing heavyweight desktop software.

• A new AI methodology called Groundsource has just been unveiled, turning millions of public reports into a massive historical dataset for predicting urban flash floods. By mapping these events using Gemini and Google Maps, a new model can now forecast sudden flooding up to 24 hours in advance, offering a vital lifeline to communities worldwide.

Jobs

• ESA is looking for two EO Data Architect and AI Experts based in Frascati for their (1) Copernicus Ground Segment Systems Section and (2) Ecosystem Development Section.

• Development Seed is looking for a Technical Project Lead based in the US who will work across their NASA portfolio.

• The European Organization for Nuclear Research (CERN) is looking for GIS Specialist and Developer based in Geneva.

• Médecins Sans Frontières (Doctors Without Borders) is looking for a GIS Data Governance Specialist based in Geneva.

• The Center for International Forestry Research (CIFOR) is looking for a Spatial Data Scientist for Machine Learning & Remote Sensing who will be based in either Kenya, Nairobi or remote.

Just for Fun

Researchers at NOAA have shared a rather striking animation of high winds over the Great Lakes, created using their experimental RRFS weather model. It provides a brilliant demonstration of DESI, a clever tool designed to help forecasters effortlessly explore, compare, and share ensemble weather guidance.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that’s just as rivetting as a Lex Fridman podcast. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Hidden Carbon: Earthquake repairs generate massive CO2 emissions.

• Spatial AI: Drones learn to measure 3D dimensions.

• Design Economics: Distinctive buildings boost surrounding property values.

• Location Data: Mobility patterns reveal true venue functions.

• Building Atlas: Open dataset provides global 3D models.

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Research you should know about

1. Earthquakes have a huge carbon footprint

When we talk about the environmental cost of buildings, we usually focus on the materials used to build them or the energy they consume. But a new study in Nature Communications shines a light on a hidden carbon cost: the emissions generated by repairing and rebuilding structures after earthquakes. By integrating seismic risk models with a massive database of construction materials, researchers have estimated that Europe’s building stock embodies nearly 14 billion tonnes of CO2 equivalent. More strikingly, the damage caused by earthquakes across the continent generates an average of 6.6 million tonnes of carbon emissions every single year— roughly the same as the annual emissions of 3.9 million cars.

The study introduces a new metric called ‘Average Annual Embodied Carbon’ (AAEC) to quantify this risk. Instead of just looking at financial losses, this measures the expected yearly carbon footprint of seismic damage. The results show a heavy concentration of this environmental burden in just a few countries. Türkiye tops the list, accounting for over half of the total annual emissions due to its high seismic activity and large number of vulnerable buildings. Italy, Romania, Greece, Spain and Bulgaria also feature prominently, meaning just six nations are responsible for about 90 per cent of Europe’s earthquake-related carbon emissions.

This research highlights a critical blind spot in our current sustainability strategies. While we are busy making buildings more energy-efficient, we often ignore the ‘embodied carbon’ that gets released when we have to fix or replace them after a disaster. The authors argue that seismic resilience is actually a form of climate action. By retrofitting buildings to withstand shocks, we aren’t just saving lives and money; we are also preventing millions of tonnes of CO2 from being pumped into the atmosphere during reconstruction. The team has released their datasets and models publicly, hoping to help policymakers integrate these hidden environmental costs into their disaster risk planning.

2. Giving drones a sense of depth

Remote sensing AI is getting sharper, yet it still struggles with a fundamental human skill: understanding 3D space. Most current models look at 2D pixels and can identify a ‘red car’, but they can’t reliably tell you its actual dimensions or how far away it is from the sensor. To bridge this gap, researchers have developed AirSpatial, a massive dataset derived from drone imagery that introduces 3D bounding boxes to the mix. By combining aerial photos with ground-level video, they have annotated thousands of vehicles with precise spatial data, allowing models to move beyond simple visual recognition to true spatial grounding where an AI can understand depth and scale.

The team used this data to train AirSpatialBot, an agent designed to recognise fine-grained vehicle attributes from the sky. The system uses a clever two-stage training strategy: it starts by learning standard 2D image understanding before fine-tuning on spatial tasks using techniques like Geometric Mapping Learning. Instead of just memorising what a specific car model looks like from above, the bot actually measures the vehicle’s length, width and height in the 3D scene. It then acts like a detective, cross-referencing these physical dimensions with a database to determine the exact make, model and even the price of the car, managing to distinguish between vehicles that look visually similar but differ in size.

In testing, AirSpatialBot significantly outperformed existing models like GeoChat, particularly in tasks requiring depth estimation and dimension measuring. The system excels at ‘target retrieval’, where a user can ask it to find a specific car (e.g. ‘white Mercedes-Benz C-Class’) and the bot locates it by calculating 3D coordinates. This approach is particularly flexible because it doesn’t require retraining when new car models come out; you simply update the reference table with the new dimensions. It’s a compelling step forward for drone-based embodied intelligence, suggesting that future aerial agents won’t just see the world as a flat image, but will navigate and understand it in full 3D.

3. The economics of good design

We all know that walking through a neighbourhood with stunning architecture feels better than walking through a sea of bland concrete, but quantifying that feeling has always been tricky. A new working paper takes a deep dive into the economics of architecture by combining a massive literature review with a new quantitative model. They found that distinctive buildings sell for about a 15 per cent premium compared to ordinary ones, confirming that people are willing to pay for good design. Perhaps more interestingly, they found a significant positive spillover effect where distinctive buildings boost the value of nearby properties by around 9 per cent. The catch is that building something unique is expensive, costing about 25 per cent more than a standard structure. This creates a classic coordination problem where developers under-invest in good design because they bear all the costs but only capture some of the value.

To understand how to fix this, the researchers modelled a neighbourhood where residents have different tastes for architecture. They ran a survey and discovered that people’s preferences for design are quite varied, roughly as diverse as their preferences for where to live within a city. This heterogeneity means that as you build more distinctive buildings, the premium people are willing to pay for them drops because you run out of ‘super-fans’ for that style. The model shows that without intervention, the market provides less distinctive architecture than is socially optimal. It turns out that the best way to solve this isn’t through complex zoning rules but through a direct Pigovian subsidy. Their simulations suggest that subsidising about 10 per cent of the construction costs for distinctive buildings creates the maximum welfare gain, balancing the extra cost against the aesthetic benefits for the community.

The study also throws cold water on some common planning tools. For instance, offering Floor Area Ratio (FAR) bonuses—letting developers build taller if they include good design—often backfires. Unless the city is already severely height-constrained, these bonuses tend to reduce welfare by distorting the housing supply. Similarly, relying on a ‘super-developer’ to build a whole district doesn’t necessarily help because they might restrict the supply of distinctive buildings to keep prices high. Designating small ‘distinctive districts’ (like conservation areas) can work well if they cover a small chunk of the neighbourhood, around 4 per cent, but expanding them too much leads to diminishing returns. The takeaway is that if we want better-looking cities, we should probably just pay for them directly rather than trying to engineer them through complex planning loop-holes.

4. Your location data knows more than you think

In the world of digital mapping, a point of interest (POI) is usually defined by static data: its name, category and coordinates. But as anyone who has ever visited a ‘quiet’ cafe that turned out to be a bustling co-working hub knows, a label doesn’t tell the whole story. To capture this nuance, researchers have introduced ME-POIs, a framework that enriches static POI data with patterns from large-scale human mobility. Instead of just knowing a place is a ‘restaurant’, the model learns its function by analysing visit sequences (whether it’s a quick lunch spot or a long-stay dinner venue).

The system uses a contrastive learning approach to align individual visit patterns with a global representation for each POI. One clever trick they employ is a ‘multi-scale distribution transfer’ mechanism. Since most places don’t get enough visits to generate robust data on their own, the model borrows insights from nearby, popular spots to fill in the gaps for less-frequented locations. This allows it to generate meaningful insights even for the ‘long tail’ of rarely visited businesses that usually get overlooked by data-hungry algorithms.

When tested on tasks like predicting opening hours, price levels and visit intent in Los Angeles and Houston, the mobility-augmented model consistently outperformed text-only baselines. For instance, adding mobility data boosted the accuracy of predicting a venue’s price level by over 75 per cent in some cases. It turns out that how people move through a city—when they arrive, how long they stay, and where they go next—reveals as much about a place’s identity as its official description does.

Geospatial Datasets

1. Global building dataset

GlobalBuildingAtlas presents the first open, globally complete dataset comprising building polygons, heights, and Level of Detail 1 (LoD1) 3D models. You can access the data here and the code here.

2. Semantic vehicle trajectory dataset

Captured via Unmanned Aerial Vehicles (UAVs) over the busy Guangzhou Bridge, the High-Density Semantic Vehicle Trajectory (HDSVT) dataset fills a gap in autonomous driving resources by providing long-duration tracking in high-density scenarios. You can access the data here.

3. Transport systems database

The African Transport Systems Database (AfTS-Db) stands as the first comprehensive, geolocated multi-modal transport database for the entire African continent. You can access the dataset here and the code here.

4. Global leaf chlorophyll content dataset

The MuSyQ Global LCC product revolutionises ecosystem monitoring by leveraging Sentinel-2 imagery to map leaf chlorophyll content at an impressive 10-m resolution. You can access the data here and the code here.

Other useful bits

• Europe’s Destination Earth project is taking climate modelling to the next level with a high-resolution ‘digital twin’ of our planet. This AI-powered simulation isn’t just about sharper weather forecasts; it’s already helping to optimise renewable energy and map urban heat, providing a crucial dashboard for understanding Earth’s complex systems.

• In its latest impact report, Planet Labs reveals how its daily Earth imagery is driving real-world change, from helping conservationists protect endangered species to spotting massive methane leaks in Texas.

• Albedo has shared a candid look at its Clarity-1 mission, proving that satellites can indeed thrive in the harsh environment of Very Low Earth Orbit (VLEO). Although the mission ended early due to hardware issues, it successfully validated the platform’s core tech, paving the way for ultra-high-resolution 10 cm imagery in the future.

• Data Center GeoSniffer is a fascinating new tool that merges AI permit scraping with satellite imagery to track critical infrastructure expansion across the US. By providing a ‘neural sniff’ of the grid, it allows users to look past the paper trail and spot physical changes like new substations and generator pads.

Jobs

• Center for International Forestry Research (CIFOR) is looking for a remote Spatial Data Scientist

• International Water Management Institute (IWMI) is looking for a Senior Geospatial Data Engineer based in Colombo, Sri Lanka

• Impact Initiatives is looking for a Senior Assessment officer based in Geneva, Switzerland.

• CERN is looking for a GIS Specialist based in Geneva, Switzerland

Just for Fun

New research reveals that space travel can actually cause an astronaut's brain to shift upwards in the skull, leading to disorientation that lingers long after they return to Earth. Understanding these ‘sensory conflicts’ is now seen as a vital step before we embark on longer missions to the Moon or Mars.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that was named by the same folks who named GPT-5.4 (Thinking) High. In any case, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Data Centres: New maps show remaining US capacity.

• SAM3 Fixes: CONCEPTBANK improves segmentation under domain drift.

• Spatial AI: Why chatbots still struggle to drive.

• Health Mapping: AI links imagery and demographics.

• WorldPop 2: New global population grids at 100 metres.

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Research you should know about

1. We’re running out of room for data centres

The recent generative AI boom is driving a massive expansion of hyperscale data centres across the United States. While we have plenty of maps showing where these facilities are currently located, a new study takes a different approach by looking at where they can actually still be built. Rather than relying on future demand forecasts or basic zoning laws, the researcher built a constraint-first geospatial framework. This means they focused entirely on the strict physical, environmental and infrastructural limits that dictate where a massive server farm can realistically operate today.

To map this out, this new study divided the country into a uniform hexagonal grid and analysed the precise characteristics of existing hyperscale sites. The models layered in a huge amount of spatial data, including high-voltage power transmission lines, local substations, summer temperature peaks, flood risks and built-up land density. By using unsupervised machine learning approaches to compare these data layers against the environments of current facilities, the analysis identified remaining regions that share the specific structural DNA needed to support new development.

The findings suggest that the US is facing a pretty tight ceiling when it comes to future hosting capacity. Both models converged on a surprisingly limited envelope of feasible land, estimating that the total physically viable capacity left is somewhere in the tens of gigawatts (roughly 25 to 100 GW), rather than the hundreds of gigawatts that some optimistic industry forecasts assume. Ultimately, this shows that the tech industry is running out of easy real estate. Exceeding this physical limit will require a fundamental overhaul of the underlying electricity system and local regulatory landscapes.

2. Taming foundation models for the real world

The recently released SAM3 model has been pretty massive for text-to-segmentation. As with the other SAMs, it allows users to segment almost anything just by typing a text prompt. However, when you take it out of its comfortable pre-training environment and deploy it in the wild, things start to break down. The main culprits are data drift and concept drift. Data drift happens when the actual visual statistics shift, which is a pretty big issue in remote sensing where aerial viewpoints and spectral signatures look nothing like standard photos. Concept drift occurs when the meaning of a label changes depending on the context (like how the word “mouse” means an animal in a nature dataset but a clicking-device-thingy in an office dataset). When these shifts happen, the model’s static text prompts no longer align with the visual evidence, leading to missed objects and incorrect masks.

To fix this without the massive computational headache of retraining the whole model, researchers have proposed a parameter-free framework called CONCEPTBANK. Instead of relying on static text prompts, this system builds a dataset-specific dictionary derived directly from the target domain’s actual statistics. It works through a clever three-stage pipeline. First, it anchors target evidence by finding visual prototypes for each class. Second, it filters out noisy outliers to ensure it is only looking at representative examples. Finally, it uses a large language model to generate a variety of synonyms and attribute descriptions, fusing the best ones together to create a calibrated text anchor that perfectly matches the target dataset. It does all of this on the fly without updating a single weight in the SAM3 backbone.

The results are quite compelling, especially for those of us working with geospatial data (which I’m guessing is you). When tested across four different remote sensing benchmarks, CONCEPTBANK boosted SAM3’s average performance by 13 percentage points compared to the vanilla version. Datasets like LoveDA and iSAID, which usually trip up standard models due to their complex aerial perspectives, saw huge improvements. It also performed exceptionally well on standard natural scene datasets. Because the system bypasses the text encoder during actual inference and just retrieves the pre-calibrated anchors, it actually speeds up the processing time by 25 per cent. Ultimately, this approach provides a highly robust, plug-and-play way to optimise massive foundation models for messy, real-world satellite imagery.

3. Why chatbots make terrible drivers

LLMs are great. And I’m sure many of you are like me - our best friends are an LLM, like my OpenClaw Billy. But LLMs have historically failed when navigating the physical world. A model that can perfectly describe a photo of a kitchen cannot necessarily figure out how to move through it or pick up a mug. This happens because these models lack ‘spatial grounding’, which is the metric understanding of geometry and physics required to turn perception into action. To solve this disconnect, a team of researchers reviewed over 2,000 papers to create a massive new taxonomy that connects standard AI agent capabilities with spatial intelligence.

Their framework divides the landscape into three axes: the specific spatial task, the agent’s cognitive capabilities, and the physical scale. When mapping existing research onto this grid, they discovered a pretty big imbalance. Roughly 68 per cent of current methods focus on ‘meso-spatial’ tasks like room-scale navigation, leaving micro-scale robotic manipulation and macro-scale geospatial analysis completely underexplored. To bridge these gaps, the authors highlight some promising architectural patterns, specifically integrating Graph Neural Networks with language models. This helps agents maintain a structured, persistent understanding of spatial relationships over time rather than relying on text alone.

Another major takeaway is the absolute necessity of ‘world models’ for safe physical deployment. Since physical mistakes can cause irreversible damage, agents need these internal simulators to predict the consequences of their actions before actually making a move. Looking ahead, the researchers pinpoint several grand challenges that stand in the way of truly autonomous spatial systems. The most glaring issue is the lack of unified cross-scale representation, meaning an AI struggles to seamlessly transition from planning a city-wide route down to the centimetre-level precision needed to open a door. Solving these structural barriers will be essential for the next generation of robotics and autonomous vehicles.

4. Mapping public health from space

Predicting public health outcomes usually requires massive amounts of census and medical data. Satellite imagery can show us the physical environment, like green spaces or industrial zones, but it misses crucial demographic context. To bridge this gap, researchers have developed a new AI model called the Geo-Context Guided Visual Transformer (GCGVT). Instead of just merging data blindly, it uses a ‘Guided Attention’ mechanism. This allows the model to look at an aerial view of a neighbourhood while being explicitly guided by specific socioeconomic factors, pointing the AI to the most relevant physical features.

The team tested their model on predicting the prevalence of nine chronic diseases, including diabetes, obesity and depression, across 14,330 historical redlining zones in the US. They fed the AI aerial photos alongside 228 variables from the American Community Survey, covering details like age, income and race. The new model achieved an impressive average predictive score of 0.85, completely outperforming standard vision-language models like CLIP. Interestingly, the researchers found that they did not need all 228 variables. By combining the aerial imagery with just two demographic categories, such as Income and Race for predicting obesity, or Age and Housing for arthritis, the model still delivered highly accurate forecasts.

The real breakthrough here is the transparency of the system. Most deep learning models operate as a black box, making them hard to trust in healthcare settings. However, this framework assigns different AI attention heads to specific demographic categories, allowing users to see exactly how the model connects the dots. For instance, when predicting depression rates, the model visually highlighted how employment data correlated to specific housing blocks, and how education levels mapped onto nearby natural areas. This gives public health officials a highly interpretable tool to map out health disparities without relying on exhaustive, expensive on-the-ground surveys.

Geospatial Datasets

1. WorldPop Global 2 dataset

The newly released WorldPop Global 2 dataset provides highly detailed, annual geospatial population estimates at a 100-metre resolution for over 240 countries and territories, spanning the years 2015 to 2030. Built using recent census information and satellite-derived settlement modelling, this open-access resource also provides age and sex breakdowns. You can access the data here.

2. Long-term night-time light dataset

The Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset provides a continuous, high-quality annual record of night-time illumination across China from 1986 to 2024. Using a two-stage deep learning framework, this 500-metre resolution map resolves common issues found in older satellite records, such as the underestimation of brilliant city centres and the blurring of intricate road networks. You can access the data here and the code here.

3. Impervious surface cover dataset

The China Impervious Surface Cover (CISC) dataset provides a 30-metre resolution map of artificial surfaces across mainland China for 2020 and 2022. It fuses together high-resolution 2-metre imagery from the Gaofen satellite series with 30-metre Landsat and SRTM data via a regionally adaptive deep learning model. You can access the data here and the code here.

4. Bioavailable strontium isotope data

If, unlike me, you’ve heard of bioavailable strontium isotopes, then you’re in luck. This compilation harmonises over 28,000 bioavailable strontium isotope (87Sr/86Sr) data points from more than 150 countries into one easily accessible resource. It draws from decades of research across soil, water, flora, and fauna samples. You can access the data here and here, and the code here.

Other useful bits

• The European Space Agency has just launched the EOPF Sentinel Zarr Explorer, a new platform that shifts massive Earth observation datasets into a new (and different) cloud-native format. This means we can now discover, visualise, and analyse satellite imagery directly in their web browsers without the headache of downloading colossal files.

• Researchers have just released PyGALAX, a new open-source Python toolkit designed to take the headache out of complex geospatial machine learning. By combining automated modelling with explainable AI, it gives users a much clearer picture of the 'why' behind spatial patterns, making it a fantastic resource for everything from urban planning to environmental monitoring.

• A new tool called the Interpretable Code-based assistant for Earth Observation (IC-EO) is making satellite data analysis accessible by translating simple, natural language questions directly into Python code. Developed by researchers alongside the European Space Agency, it allows even non-experts to tackle complex tasks like wildfire assessments and land-use mapping with impressive accuracy and transparency.

• Google Earth is tackling the headache of city gridlock by letting urban planners import their own custom traffic data directly onto the globe. By mixing this with built-in layers like vehicle trip counts and signal performance, it provides a new way of pinpointing bottlenecks.

Jobs

• Mapbox is looking for a Technical Support Engineer based in China

• UNEP - United Nations Environment Programme is looking for a Spatial Data Application Developer based in Nairobi, Kenya

• International Water Management Institute (IWMI) is looking for a Senior Geospatial Data Engineer based in Colombo, Sri Lanka

Just for Fun

This map outlines the European Union's envisaged transport and trade corridors across Africa.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan

Subscribe now

Hey guys, here’s this week’s edition of the Spatial Edge — a newsletter that’s more riveting than a Bad Bunny halftime performance. As usual, the aim is to make you a better geospatial data scientist in less than five minutes a week.

In today’s newsletter:

• Spatial Mapping: ANPs improve biomass uncertainty estimates.

• Image Sharpening: Synthetic data enhances hyperspectral resolution.

• Landslide Detection: Deep learning improves hazard monitoring.

• Malaria Threat: Climate change disrupts disease eradication.

• Building Heights: New deep learning estimation dataset.

Subscribe now

Research you should know about

1. Knowing when to doubt the data

A lot of folks use standard machine learning models like Random Forest or XGBoost to map environmental variables like aboveground biomass. These models are fantastic at making point predictions, but a new paper highlights that they are systematically overconfident when estimating uncertainty. When you use these standard baseline configurations, you end up with uncertainty estimates that fail to capture local spatial variability. For instance, if you’re mapping a landscape with sharp transitions between forests and cleared fields, traditional models generate the exact same narrow uncertainty interval they would anywhere else. They completely miss the local context. This basically means development organisations and carbon market verifiers could be making big conservation decisions using highly inaccurate confidence levels.

The researchers present Attentive Neural Processes (ANPs) as a much smarter alternative for spatial mapping. ANPs use an episodic training structure to learn spatial patterns directly, improving upon older methods that treat every observation in isolation. The model takes a sample area, splits the data, and learns to predict targets conditioned on the surrounding context. If the nearby data points are tightly clustered, the model assigns low uncertainty. If the context points are scattered and highly variable, it naturally expands its prediction intervals. The model essentially meta-learns how to adapt to local landscape heterogeneity on the fly.

The results show a massive difference between the two approaches. Across diverse forest types, the ANP consistently achieved well-calibrated uncertainty scores, whereas the traditional models fell way below expected coverage targets. When the models had to transfer their predictions to entirely new geographic regions, XGBoost coverage dropped to around 15 per cent because it simply could not adapt to the distributional shift. The ANP maintained strong coverage even as overall accuracy dipped. It is a pretty big reminder that having a model that knows when it is unsure is just as crucial as having one that makes accurate predictions.

2. Sharpening images with ‘dead leaves’

Hyperspectral imaging is awesome for identifying materials from space, but it usually suffers from a trade-off where you get rich spectral detail but poor spatial resolution. Deep learning can normally sharpen these images, but there’s a catch. There’s always a catch… These models typically need high-resolution ‘ground truth’ data to learn from, which simply doesn’t exist for most real-world scenarios. To get around this, researchers from Télécom Paris have come up with a clever unsupervised approach that relies on fully synthetic training data generated by something called the ‘dead leaves model.’

The main idea is to break the hyperspectral image down into ‘abundances’ (maps showing where specific materials are) and ‘endmembers’ (the spectral signature of those materials). The team then trains a super-resolution neural network solely on synthetic abundance maps created by simulating dead leaves falling on a canvas. While this sounds abstract, these random, overlapping shapes create statistical properties like edges and flat regions that mimic real-world abundance maps surprisingly well. This allows the network to learn how to sharpen features without ever seeing a real high-resolution image during training.

When tested on the standard ‘Urban’ dataset, this method didn’t just work; it actually outperformed state-of-the-art supervised methods like MCnet and HSISR. This is a pretty big deal because the competitors were trained on data with ground truth, while this model learned entirely from synthetic shapes. It demonstrates that we don’t always need expensive or impossible-to-acquire real-world datasets to train effective AI models. By using the dead leaves model to simulate texture and structure, the researchers provided a robust proof of concept for sharpening hyperspectral imagery in a completely unsupervised way.

3. Teaching AI to spot landslides

Landslides pose a massive threat to infrastructure in geologically unstable regions, yet our traditional methods for monitoring them (like ground surveys and visual inspections) are often too slow to be effective. To address this gap, a new paper in Scientific Reports looks into a new deep learning approach that employs enhanced Deep Convolutional Neural Networks (DCNNs) to detect landslides with high precision.

The researchers introduced several key modifications to make the system smarter. They integrated a spatial attention mechanism and attention-based Global Average Pooling, which essentially help the AI focus on the most critical parts of an image rather than getting distracted by irrelevant background noise. To further refine the training process, they utilised the Lookahead Adam optimizer and optimised learning rate schedules. These technical tweaks were designed to improve how the model extracts features and converges on a solution, ensuring it can generalise well across different types of terrain.

The performance metrics for this new approach are pretty impressive. In experiments using the Kaggle Landslide Dataset, the model achieved a testing accuracy of 95.8 per cent, with similar success observed when testing against the NASA Landslide Inventory. This level of accuracy suggests that attention-driven DCNNs could significantly outperform conventional assessment methods. By offering a reliable and efficient tool for automated monitoring, this technology could be crucial for early detection systems that help mitigate risks to communities living in danger zones. You can access the code here.

4. Climate chaos threatens malaria eradication

After decades of progress, the global fight against malaria has stalled, and climate change is emerging as a massive new hurdle. A new study in Nature has quantified this threat by looking at both the direct ecological impacts of a warmer world (like mosquito breeding rates) and the indirect, ‘disruptive’ impacts of extreme weather. By integrating 25 years of data on climate, disease burden and socioeconomic factors, the researchers project that climate change could lead to an additional 123 million malaria cases and 532,000 deaths in Africa between 2024 and 2050.

Interestingly, the study finds that it isn’t just about mosquitoes enjoying warmer weather. In fact, 79 per cent of the additional cases and 93 per cent of the extra deaths will likely be driven by extreme weather events like floods and cyclones. These disasters destroy housing, cut off access to healthcare and disrupt vector control programs, creating perfect conditions for outbreaks in areas that are already struggling. The research shows that while some regions might actually see a decrease in transmission due to excessive heat, this is far outweighed by the intensification of malaria in existing endemic zones, particularly in densely populated countries like Nigeria and Uganda.

This analysis challenges the traditional focus on ‘range expansion’, showing that 99 per cent of the additional burden will hit communities that already suffer from malaria, rather than spreading to new areas. The compounding effect of ecological changes and infrastructure disruption creates a ‘double whammy’ for vulnerable populations. To safeguard the goal of eradication, the authors argue we need to move beyond standard control measures and invest heavily in climate-resilient health systems, ensuring that clinics, supply chains and mosquito control efforts can withstand the increasingly volatile weather of the coming decades. You can access the code here.

Geospatial Datasets

1. Building height estimation dataset

M4Heights is a new benchmark dataset designed to advance building height estimation through deep learning, covering vast landscapes across Estonia, the Netherlands, and Switzerland. You can access the data here and the code here.

2. Carbon, water, and energy fluxes dataset

The Unified FLUXes (UFLUX) ensemble provides a globally consistent dataset of carbon, water, and energy fluxes, derived from a robust combination of eddy covariance data, satellite observations, and machine learning. You can check out the list of datasets here and the code here.

3. Automated crop monitoring dataset

This comprehensive dataset addresses the critical need for automation in crop monitoring by providing high-quality, diverse data for six crop species (sugar beet, spring wheat, sweet corn, soybean, potato, and wheat-faba bean intercrop). You can access the data here and the code here.

4. Root zone soil moisture dataset

TAMSAT-SM provides a reliable, long-term dataset of root zone soil moisture across Africa, designed to support operational agricultural drought monitoring. Spanning from 1983 to the present at a 0.25° spatial resolution, it uses the JULES land surface model forced with TAMSAT rainfall and NCEP reanalysis data, tuned to satellite observations. You can access the data here and the code here (registration required).

Other useful bits

• A new Earth Engine tutorial shows how to replicate this pretty awesome GOES GeoColor visualisation using Python. By blending daytime imagery with night-time city lights and infrared cloud data, this guide helps users create seamless animations that beautifully capture the transition from day to night across the winter solstice.

• Planet is helping to illuminate the world's ‘dark fleets’ with their new Maritime Domain Awareness solution. By combining daily satellite imagery with AI detection, this tool tracks ships even when they switch off their AIS signals.

• Google Earth is testing a new experimental feature that lets users bring their own KML, KMZ, and GeoJSON files directly onto the globe. This update makes it easier than ever to securely layer custom data over Google’s satellite imagery, helping users make smarter decisions, faster.

• A new tool called GeoSpy AI is turning heads with its ability to pinpoint the exact location of a social media photo in just two seconds. It’s pretty freaky… It maps the results in full 3D, offering a powerful (if slightly unnerving) demonstration of just how much information can be gleaned from a single image.

Jobs

• Development Seed is looking for a remote Technical Project Lead

• FAO - Food and Agriculture Organization of the United Nations is looking for a Geospatial Data Scientist based in Rome, Italy

• WHO - World Health Organization is looking for a remote Epidemic Intelligence and GIS Analyst: https://unjobs.org/vacancies/1772554525336

• WFP - World Food Programme is looking for a remote Earth Observation Analyst

Just for Fun

A stunning detailed map of the world’s 1,180 micro-tectonic plates.

That’s it for this week.

I’m always keen to hear from you, so please let me know if you have:

• new geospatial datasets

• newly published papers

• geospatial job opportunities

and I’ll do my best to showcase them here.

Yohan