Arrechea-Castillo, Darwin AlexisSolano-Correa, Yady TatianaMuñóz-Ordóñez, Julián FernandoCamacho-De Angulo, Yineth VivianaSánchez-Barrera, EstivenFigueroa-Casas, ApolinarPencue-Fierro, Edgar Leonairo2024-09-122024-09-122023-06-152024-09-11D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; Y.V. Camacho-De Angulo; E. Sánchez-Barrera; A. Figueroa-Casas; E.L. Pencue-Fierro, "Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252505 (15 June 2023). DOI: https://doi.org/10.1117/12.2664340.https://hdl.handle.net/20.500.12585/12735The Las Piedras River sub-basin, located in the department of Cauca, Colombia, is very important for the region, especially for the capital (Popayán). This is because this sub-basin contributes around 68.17% of the water supply for the city. To guarantee continuity of this resource, good management of the Water Ecosystem Services (WES) must be carried out. To this aim, periodic environmental assessments of the water resource in the region are necessary. Such Environmental Assessment WES (EAWES) is possible when an accurate and up-to-date land cover map is available. However, obtaining such a product is quite complex due to the heterogeneous conditions both in the land cover and orography of the studied region. Another impacting factor is the weather conditions of the region, that make it difficult to access the areas and/or to acquire information for land cover mapping. This research proposes a robust model, based on deep learning and Sentinel2 satellite images, able to perform a land cover classification with reliable accuracy (>90%) at a low computational cost. A variant of a LeNet convolutional neural network has been used together with features extracted from original spectral bands, radiometric indices and a digital elevation map. Preliminary results show an overall accuracy of 95.49% in the training data and 96.51% in the validation one.9 páginasapplication/pdfenginfo:eu-repo/semantics/article10.1117/12.2664340Deep learningConvolutional Neural Networks (CNNs)Remote SensingLand Use and Land CoverSentinel-2info:eu-repo/semantics/closedAccessUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarLEMB