CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2
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.
Authors
- Fernando Prudencio
- B. Willems (ORCID: https://orcid.org/0000-0002-0666-4655)
- David Montero (ORCID: https://orcid.org/0000-0002-9010-3286)
- Jhomira Loja
- Wendy Espinoza (ORCID: https://orcid.org/0000-0002-6061-814X)
- Luis Ysuhuaylas
- Karen Gonzales
- Raúl Loayza‐Muro (ORCID: https://orcid.org/0000-0002-1312-311X)
- Roy Yali (ORCID: https://orcid.org/0000-0003-4542-3755)
- Gonzalo Mateo‐García (ORCID: https://orcid.org/0000-0002-0569-393X)
- César Aybar (ORCID: https://orcid.org/0000-0003-2745-9535)
- Lesly Bautista
- Fernando Herrera
- Evelin Mamani
- Maria Quiñonez
- Antonio Limas
- Martin Leyva
- Daryl Ayala
- Julio Contreras (ORCID: https://orcid.org/0009-0001-5408-7055)
- Luis Gómez-Chova
- Alejandro Alcántara
- Jeanett Valladares
- Lucy A Flores
- R Pantoja Fajardo
Institutions
- Federico Villarreal National University (PE)
- Universitat de València (ES)
- Universidad de Salamanca (ES)
- National University of San Marcos (PE)
- Universidad Peruana Cayetano Heredia (PE)
- German Centre for Integrative Biodiversity Research (DE)
- Universidad Nacional Agraria de la Selva (PE)
- Universidad Nacional Santiago Antúnez de Mayolo (PE)
- Universidad Nacional Agraria La Molina (PE)
- Leipzig University (DE)
Publication Details
- Journal
- Data in Brief
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1016/j.dib.2024.110852
- Citations
- 10
- Primary Topic
- Remote-Sensing Image Classification
- Type
- article
- Field-Weighted Citation Impact
- 3.20
Funders
- Ministerio de Ciencia, Innovación y Universidades
- European Space Agency
- Ministerio de Ciencia e Innovación
- Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
- Agencia Estatal de Investigación