A new global remote sensing dataset with 1.8 million vector-annotated instances across 10 land cover classes, spanning 79 regions on six continents, for benchmarking vector-based land cover mapping.
LandCoverNet: A global benchmark land cover classification training dataset
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abstract
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verified by consensus among three human annotators.
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IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization
A new global remote sensing dataset with 1.8 million vector-annotated instances across 10 land cover classes, spanning 79 regions on six continents, for benchmarking vector-based land cover mapping.