Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

Arrechea-Castillo, Darwin AlexisSolano-Correa, Yady TatianaMuñoz-Ordóñez, JulianPencue-Fierro, Edgar LeonairoSánchez-Barrera, Estiven2024-09-122024-09-122023-06-152024-09-11D.A. Arrechea-Castillo; Y. T. Solano-Correa; J.F. Muñoz-Ordóñez; E.L. Pencue-Fierro; E. Sánchez-Barrera, "Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks," in Proc. SPIE 15525, Geospatial Informatics XIII, 1252503 (15 June 2023). DOI: https://doi.org/10.1117/12.2664029.https://hdl.handle.net/20.500.12585/12734The Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geography complexity and social dynamics of the area make it difficult to monitor and capture data. One example of such areas can be found in the South-West of Colombia, in the Las Piedras River sub-basin. The National Administrative Department of Statistics in Colombia (DANE in Spanish) aims at mapping the population and houses in dispersed and difficult-to-access rural settlements in an accurate and continuous way. Nevertheless, there are several difficulties (derived from the in-situ way of collecting the data) that prevent such data from being generated. This research presents a methodology to carry out an updated mapping of rural areas with high spatial resolution data coming from PlanetScope (3m). Such a mapping considers the dynamics of housing growth, focusing on dispersed and difficult-to-access rural settlements. To this aim, Convolutional Neural Networks (CNNs) are used together with PlanetScope data, allowing to account for average houses size (≥12𝑚��2 ) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity9 páginasapplication/pdfengMapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networksinfo:eu-repo/semantics/article10.1117/12.2664029Rural settelmentDeep learningRemote sensingPlanetScopeSDGsinfo:eu-repo/semantics/closedAccessUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarLEMB