VibrantForests produces coherent 10m wall-to-wall estimates of multiple forest structure attributes across the US by applying satellite models trained on lidar samples.
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Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
Releases a publicly available, collocated multi-sensor dataset of Landsat, Sentinel-1, GOES-R and microwave observations for urban heat studies across 48 cities.
citing papers explorer
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Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
VibrantForests produces coherent 10m wall-to-wall estimates of multiple forest structure attributes across the US by applying satellite models trained on lidar samples.
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Earth Embeddings Reveal Diverse Urban Signals from Space
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
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Urban Heat MiniCubes: An AI-Ready dataset for urban heat research
Releases a publicly available, collocated multi-sensor dataset of Landsat, Sentinel-1, GOES-R and microwave observations for urban heat studies across 48 cities.