Researchers released AgroBench, a reproducible multimodal benchmark that converts U.S. county-level crop yield statistics into weakly supervised pixel-level crop time series, containing over 13 millio…
A deep learning model called U-Net-ID mapped 6,515,705 large trees across roughly 78.7% of the Sierra Nevada Floristic Province using 2020 sub-meter aerial imagery, according to a study published on a…
A developer built Fieldhand, an agent fleet for the All Things Agentic Hackathon that helps farmers by trying to disprove its own satellite-based crop diagnoses. The system uses a Watchman to select f…
Researchers introduced a loss scaling coefficient α to align deep learning models for woody clearing detection with end-user Fβ metrics, increasing precision by 1.85x or recall by 1.12x on 7 years of …
The Allen Institute for AI (AI2) announced that OlmoEarth Studio now supports custom embedding exports, allowing users to compute and download embedding vectors from its open-source OlmoEarth foundati…
Researchers introduced a masked evidential loss for Deep Evidential Regression (DER) to estimate forest height from multimodal satellite imagery, achieving predictive performance comparable to a deter…
A developer's experiment replacing 30 lines of Rasterio with 30 lines of TorchGeo for a Sentinel-2 tile workflow (loading, clipping to AOI, computing NDVI, preparing patches) found both libraries achi…
Google's new Nano Banana AI image generator in Google Earth can create realistic satellite and aerial images that are ripe for misuse, as demonstrated by Digital Digging's Henk van Ess, who generated …
A new benchmark dataset, Similar-but-Different, from Caleb Robinson and colleagues at Microsoft, shows that most pre-trained geospatial foundation models fail to use non-visible multispectral bands, g…
AI and modern tooling are reshaping geospatial work by enabling free satellite data, cloud platforms, and machine learning models to process imagery at scale. Practical workflows now use open-source l…
A new PyTorch-based optimization method reconstructs high-resolution Sentinel-2 reflectance scenes from approximately 30 low-resolution observations by exploiting sub-pixel jitter between repeat passe…
Researchers have developed the first vision transformer-based method for mapping coastal algal blooms using Landsat-8/9 and Sentinel-2 satellite imagery. The deep learning approach outperformed tradit…
Researchers released Biomazon, a 20-meter multimodal benchmark dataset covering the Amazon Basin that pairs GEDI relative height (RH) profiles and aboveground biomass density (AGBD) targets with satel…
Researchers from multiple institutions evaluated machine learning and deep learning models for satellite-derived bathymetry using Sentinel-2 imagery, finding that preserving spatial continuity during …
Researchers have developed AGFlow, a spatiotemporal flow-matching model that fuses asynchronous Sentinel-1 radar and Sentinel-2 optical satellite data to remove clouds and reconstruct missing time-ser…