CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2

Aybar C.; Bautista L.; Montero D.; Contreras J.; Ayala D.; Prudencio F.; Loja J.; Ysuhuaylas L.; Herrera F.; Gonzales K.; Valladares J.; Flores L.A.; Mamani E.; Quiñonez M.; Fajardo R.; Espinoza W.; Limas A.; Yali R.; Alcántara A.; Leyva M.; Loayza-Muro R.; Willems B.; Mateo-García G.; Gómez-Chova L. · Data in Brief (2024) · DOI: 10.1016/j.dib.2024.110852

Resumen

Detecting and screening clouds is the first step in most optical remote sensing analyses. Cloud formation is diverse, presenting many shapes, thicknesses, and altitudes. This variety poses a significant challenge to the development of effective cloud detection algorithms, as most datasets lack an unbiased representation. To address this issue, we have built CloudSEN12+, a significant expansion of the CloudSEN12 dataset. This new dataset doubles the expert-labeled annotations, making it the largest cloud and cloud shadow detection dataset for Sentinel-2 imagery up to date. We have carefully reviewed and refined our previous annotations to ensure maximum trustworthiness. We expect CloudSEN12+ will be a valuable resource for the cloud detection research community.

Instituciones: Universidad Peruana Cayetano Heredia; Universidad Nacional Mayor de San Marcos; Universidad Nacional Agraria La Molina; Universidad Nacional Federico Villarreal; Universidad Nacional Agraria de la Selva; Universidad Nacional Santiago Antúnez de Mayolo