ArticleScientific data2022
CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhanced Cloud Detection Using a Unified Multimodal Data Fusion Approach in Remote Images.Sensors (Basel, Switzerland) · 2025Article
- All-day cloud property and occurrence probability dataset based on satellite remote sensing data.Scientific data · 2025Article
- DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts.Scientific data · 2025Article
- SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution.Scientific data · 2024Article
- Global overview of usable Landsat and Sentinel-2 data for 1982-2023.Data in brief · 2024Article
- CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2.Data in brief · 2024Article
- Global flood extent segmentation in optical satellite images.Scientific reports · 2023Article
- CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.Scientific data · 2022Article
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Authors and funding
18 authors.
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Abstract
Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples.
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