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<title>S4A-CyL: A Sentinel-2 Time Series Dataset for Deep Learning in Agriculture (2020-2024)</title>
<creator>Pascual García, Rodrigo</creator>
<creator>Diez Pastor, José Francisco</creator>
<creator>Latorre Carmona, Pedro</creator>
<creator>Pérez Núñez, Pablo</creator>
<creator>Camps-Valls, Gustau</creator>
<creator>Aroca Fernández, José Manuel</creator>
<subject>Deep learning</subject>
<subject>Sentinel-2</subject>
<subject>Time series</subject>
<subject>Agriculture</subject>
<subject>Crop type classification</subject>
<subject>Castilla y León, Spain</subject>
<subject>Land parcel</subject>
<subject>Earth Observation</subject>
<subject>Remote sensing</subject>
<subject>SIGPAC</subject>
<description>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.&#xd;
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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.&#xd;
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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.</description>
<date>2025-06-12</date>
<date>2025-06-12</date>
<date>2025-06-12</date>
<type>dataset</type>
<identifier>https://hdl.handle.net/10259/10551</identifier>
<identifier>10.71486/q4yz-p373</identifier>
<identifier>https://ss3.scayle.es:443/riubu-1/Admirable-2025/S4A-CyL_Dataset_2020-2024.zip</identifier>
<language>eng</language>
<relation>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/</relation>
<rights>http://creativecommons.org/licenses/by/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Atribución 4.0 Internacional</rights>
<coverage>north=41.66667; west=-4.25; name=Castilla y León, Spain</coverage>
<coverage>start=2020-01-01; end=2024-12-31</coverage>
<publisher>Universidad de Burgos</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>