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LandCoverNet: A global benchmark land cover classification training dataset

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arxiv 2012.03111 v1 pith:VYSLORWO submitted 2020-12-05 cs.CV cs.LG

Hamed Alemohammad , Kevin Booth This is my paper · ORCID
classification cs.CVcs.LG
keywords coverlandglobalclassificationdatasettraininglandcovernetsentinel-2
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    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.

  2. Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A plug-and-play test-time optimization method improves zero-shot open-vocabulary segmentation on specialized-domain datasets by jointly tuning per-category text embeddings and aggregating visual features.

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