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

  • 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
  • University of Valencia
  • Univ. Nacional Mayor de San Marcos
  • Centro de Competencias del Agua
  • Universidad Peruana Cayetano Heredia
  • Fakultät für Chemie und Mineralogie, Universität Leipzig
  • German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
  • Universidad Nacional Agraria La Molina
  • Universidad Nacional Federico Villarreal
  • Universidad Nacional 'Santiago Antúnez de Mayolo
  • Universidad Nacional Agraria de la Selva-Perú
  • Salamanca University

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish
Article number110852
JournalData in Brief
Volume56
DOIs
StatePublished - Oct 2024

Keywords

  • Cloud shadow
  • Global dataset
  • IRIS
  • Sentinel-2
  • Thin cloud
  • U-net

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