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arxiv:2012.03111

LandCoverNet: A global benchmark land cover classification training dataset

Published on Dec 5, 2020
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Abstract

LandCoverNet is a global training dataset for land cover classification utilizing Sentinel-2 imagery at 10m resolution, with labels verified by consensus among annotators.

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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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