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LandCoverNet: A global benchmark land cover classification training dataset
December 05, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Hamed Alemohammad, Kevin Booth
arXiv ID
2012.03111
Category
cs.CV: Computer Vision
Cross-listed
cs.LG
Citations
53
Venue
arXiv.org
Last Checked
5 months ago
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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