2026-04-20T21:14:28Zhttps://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 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. eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Deep learning Sentinel-2 Time series Agriculture Crop type classification Castilla y León, Spain Land parcel Earth Observation Remote sensing SIGPAC S4A-CyL: A Sentinel-2 Time Series Dataset for Deep Learning in Agriculture (2020-2024) dataset