2026-04-20T19:38:57Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/105512025-07-24T11:00:47Zcom_10259_2604col_10259_5684

Pascual García, Rodrigo Diez Pastor, José Francisco Latorre Carmona, Pedro Pérez Núñez, Pablo Camps-Valls, Gustau Aroca Fernández, José Manuel north=41.66667; west=-4.25; name=Castilla y León, Spain start=2020-01-01; end=2024-12-31 2025-06-12T12:14:29Z 2025-06-12T12:14:29Z 2025-06-12 https://hdl.handle.net/10259/10551 10.71486/q4yz-p373 https://ss3.scayle.es:443/riubu-1/Admirable-2025/S4A-CyL_Dataset_2020-2024.zip This dataset, "S4A-CyL," provides a comprehensive, multi-annual (2020-2024) collection of analysis-ready data patches for the Castilla y León region in Spain. It is specifically designed to support the development and validation of deep learning models for agricultural applications, with a primary focus on crop type classification. The dataset integrates dense Sentinel-2 L2A time series with meticulously processed agricultural parcel geometries and harmonized crop type labels derived from the Spanish Land Parcel Identification System (SIGPAC). A key feature is the implementation of a persistent parcel identification system, ensuring the temporal traceability of agricultural plots across the five-year period. The data is structured in a format inspired by the Sen4AgriNet project, with individual NetCDF files for each spatial patch. Each patch is a self-contained unit that includes the multi-spectral Sentinel-2 time series alongside the corresponding parcel ID and crop type reference layers. This work is part of the project: "Uso de imágenes Sentinel para la monitorización de prácticas agrícolas y su contribución a la iniciativa «4 por 1000» de incremento de carbono orgánico en el suelo" (TED2021-131638B-I00), from the "proyectos de transición ecológica y digital" (TED) program of 2021, funded by the Spanish Ministry of Science and Innovation, through the "Plan de recuperación, transformación y resiliencia" of the European Union. text/plain application/zip eng Universidad de Burgos Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Deep learning Sentinel-2 Time series Agriculture Crop type classification Castilla y León, Spain Land parcel Earth Observation Remote sensing SIGPAC Inteligencia artificial Agricultura Artificial intelligence Agriculture S4A-CyL: A Sentinel-2 Time Series Dataset for Deep Learning in Agriculture (2020-2024) dataset info:eu-repo/semantics/acceptedVersion info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-131638B-I00/ES/Uso de imágenes SENTINEL para la monitorización de prácticas agrícolas y su contribución a la iniciativa "4 por 1000 de incremento de carbono orgánico en el suelo/SEN4CFARMING/ 2025