CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2
Year: 2024
Type: data-paper
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.
Source: Data in Brief
Authors: Cesar Aybar, Lesly Bautista, David Montero, Julio Contreras, Daryl Ayala, Fernando Prudencio, Jhomira Loja, Luis Ysuhuaylas, Fernando Herrera, Karen Gonzales, Jeanett Valladares, Lucy A. Flores, Evelin Mamani, Maria Quiñonez, Rai Fajardo, Wendy Espinoza, Antonio Limas, Roy Yali, Alejandro Alcántara, Martin Leyva, Raúl Loayza-Muro, Bram Willems, Gonzalo Mateo-García, Luis Gómez-Chova
Institutions: Universitat de València, National University of San Marcos, Universidad Peruana Cayetano Heredia, German Centre for Integrative Biodiversity Research, Leipzig University, Universidad Nacional Agraria La Molina, Federico Villarreal National University, Universidad Nacional Santiago Antúnez de Mayolo, Universidad Nacional Agraria de la Selva, Universidad de Salamanca
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $850
OpenAlex ID: W4401690623