[Dataset Title/Name]: Sentinel-2 10m Land Use/Land Cover Time Series

Contact Details

galago@live.se

Dataset description

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.

The algorithm generates LULC predictions for nine classes, described in detail below.

The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2022 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2022.

Earth Engine Snippet if dataset already in GEE

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var asset_name = ee.ImageCollection("path to your collection")

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Keywords

land cover, land use

Code of Conduct

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samapriya / awesome-gee-community-datasets