Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning

Darwin A. Arrechea-Castillo; Yady T. Solano-Correa; Julián F. Muñoz-Ordóñez; Yineth V. Camacho-De Angulo; Estiven Sánchez-Barrera; Apolinar Figueroa-Casas; Edgar L. Pencue-Fierro

doi:10.1117/12.2664340

15 June 2023 Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning

Darwin A. Arrechea-Castillo, Yady T. Solano-Correa, Julián F. Muñoz-Ordóñez, Yineth V. Camacho-De Angulo, Estiven Sánchez-Barrera, Apolinar Figueroa-Casas, Edgar L. Pencue-Fierro

Author Affiliations +

Proceedings Volume 12525, Geospatial Informatics XIII ; 1252505 (2023) https://doi.org/10.1117/12.2664340
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The 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.

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Darwin A. Arrechea-Castillo, Yady T. Solano-Correa, Julián F. Muñoz-Ordóñez, Yineth V. Camacho-De Angulo, Estiven Sánchez-Barrera, Apolinar Figueroa-Casas, and Edgar L. Pencue-Fierro "Land cover classification of Andean sub-basins in Colombia based on Sentinel-2 satellite images and deep learning", Proc. SPIE 12525, Geospatial Informatics XIII , 1252505 (15 June 2023); https://doi.org/10.1117/12.2664340

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