cog earth observation environmental geospatial land cover land use machine learning mapping planetary satellite imagery stac sustainability
This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution for the years 2017 - 2021. Each year is generated from Impact Observatory’s deep learning AI land classification model using a massive training dataset of billions of human-labeled image pixels. The global maps were produced by applying this model to every Sentinel-2 scene, processing over 400,000 Earth observations per year. Leaders in governments, NGOs, finance and industry need trustworthy, actionable information about the changing world to understand opportunities, identify threats, and measure the impacts of actions. Many of the most useful applications of LULC maps require the ability to measure changes in land use and land cover over time. With a time-series of LULC maps, monitoring of deforestation, urban expansion, agricultural land conversion, and surface water scarcity all become possible. The algorithm generates LULC predictions for 9 classes globally. These classifications include Built, Crops, Trees, Water, Rangeland, Flooded Vegetation, Snow/Ice, Bare Ground, and Clouds.
Annually, each January
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10m Annual Land Use Land Cover (9-class) was accessed on
DATE from https://registry.opendata.aws/io-lulc.
aws s3 ls --no-sign-request s3://io-10m-annual-lulc/
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