Antonio Briseño Montes;Joaquin Salas;Elio Atenogenes Villaseñor Garcia;Ranyart Rodrigo Suarez;Danielle Wood
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Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning
Understanding human settlements' geographic location and extent can support decision-making in resource distribution, urban growth policies, and natural resource protection. This research presents an approach to assess human settlement sprawl using labeled multispectral satellite image patches and Convolutional Neural Networks (CNN). By training deep learning classifiers with a dataset of 5,359,442 records consisting of satellite images and census data from 2010, we evaluate sprawl for settlements across the country. The study focuses on major cities in Mexico, comparing ground truth results for 2015 and 2020. EfficientNet-B7 achieved the best performance with a ROC AUC of 0.970 and a PR AUC of 0.972 among various CNN architectures evaluated. To evaluate human settlement sprawl, we introduce an information-based metric that offers advantages over entropy-based alternatives.
期刊介绍:
IEEE Latin America Transactions (IEEE LATAM) is an interdisciplinary journal focused on the dissemination of original and quality research papers / review articles in Spanish and Portuguese of emerging topics in three main areas: Computing, Electric Energy and Electronics. Some of the sub-areas of the journal are, but not limited to: Automatic control, communications, instrumentation, artificial intelligence, power and industrial electronics, fault diagnosis and detection, transportation electrification, internet of things, electrical machines, circuits and systems, biomedicine and biomedical / haptic applications, secure communications, robotics, sensors and actuators, computer networks, smart grids, among others.