The 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 (3 m). 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 (≥12 𝑚2) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity.
KEYWORDS: Education and training, Image classification, Land cover, Data modeling, Clouds, Satellites, Satellite imaging, Deep learning, Remote sensing, Performance modeling
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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