Title
CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2
Date Issued
01 October 2024
Access level
open access
Resource Type
data paper
Author(s)
Aybar C.
Aybar C.
Aybar C.
Bautista L.
Bautista L.
Montero D.
Montero D.
Contreras J.
Contreras J.
Ayala D.
Prudencio F.
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.
Leyva M.
Loayza-Muro R.
Loayza-Muro R.
Willems B.
Willems B.
Mateo-GarcÃa G.
Gómez-Chova L.
Universitat de València
Universidad Nacional Mayor de San Marcos
Centro de Competencias del Agua
Universidad Nacional Mayor de San Marcos
Universidad Peruana Cayetano Heredia
Universität Leipzig
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
Universidad Nacional Mayor de San Marcos
Universidad Peruana Cayetano Heredia
Universidad Nacional Agraria La Molina
Universidad Nacional Mayor de San Marcos
Universidad Peruana Cayetano Heredia
Universidad Nacional Mayor de San Marcos
Universidad Nacional Mayor de San Marcos
Universidad Nacional Mayor de San Marcos
Universidad Nacional Mayor de San Marcos
Federico Villarreal National University
Universidad Nacional Mayor de San Marcos
Universidad Nacional Mayor de San Marcos
Universidad Nacional Santiago Antúnez de Mayolo
Universidad Nacional Agraria de la Selva
Universidad Nacional Mayor de San Marcos
Universidad Nacional Mayor de San Marcos
Universidad de Salamanca
Universidad Nacional Mayor de San Marcos
Centro de Competencias del Agua
Universidad Peruana Cayetano Heredia
Centro de Competencias del Agua
Universidad Peruana Cayetano Heredia
Centro de Competencias del Agua
Universidad Peruana Cayetano Heredia
Universitat de València
Universitat de València
Abstract
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.
Volume
56
Subjects
Scopus EID
2-s2.0-85202148950
Source
Data in Brief
ISSN of the container
23523409
Sources of information:
Scopus
Directorio de Producción CientÃfica