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Semantic segmentation of land cover classes is fundamental for agriculturaland economic development work, from sustainable forestry to urban planning, yetexisting training datasets have significant limitations. To generate an openand comprehensive training library of high resolution Earth imagery and highquality land cover classifications, public Sentinel-2 data at 10 m spatialresolution was matched with accurate GlobeLand30 labels from 2010, which werefiltered by agreement with an intermediary Sentinel-2 classification at 20 mproduced during atmospheric correction. Scene-level classifications werepredicted by Random Forests trained on valid reflectance data and the filteredlabels, and achieved over 80% model accuracy for a variety of locations.Further work is required to aggregate individual scene classifications forannual labels and to test the approach in more locations, before crowdsourcinghuman validation. The goal is to create a sustained community-wide effort togenerate image labels not only for land cover, but also very specific imagesfor major agriculture crops across the world and other thematic categories ofinterest to the global development community.

Generating a Training Dataset for Land Cover Classification to Advance Global Development