CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Aybar C.; Ysuhuaylas L.; Loja J.; Gonzales K.; Herrera F.; Bautista L.; Yali R.; Flores A.; Diaz L.; Cuenca N.; Espinoza W.; Prudencio F.; Llactayo V.; Montero D.; Sudmanns M.; Tiede D.; Mateo-García G.; Gómez-Chova L. · Scientific Data (2022) · DOI: 10.1038/s41597-022-01878-2

Resumen

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.

Instituciones: Universidad Nacional Mayor de San Marcos; Pontificia Universidad Católica del Perú