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Article

Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning

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CICATA Querétaro, Instituto Politécnico Nacional, Cerro Blanco 141, Colinas del Cimatario, Querétaro 76090, Mexico
2
Instituto Nacional de Estadística y Geografía, Héroe de Nacozari Sur 2301, Jardines del Parque, Aguascalientes 20276, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Victor Mesev
Remote Sens. 2021, 13(18), 3603; https://doi.org/10.3390/rs13183603
Received: 29 June 2021 / Revised: 1 August 2021 / Accepted: 6 August 2021 / Published: 10 September 2021
One of the challenges in the fight against poverty is the precise localization and assessment of vulnerable communities’ sprawl. The characterization of vulnerability is traditionally accomplished using nationwide census exercises, a burdensome process that requires field visits by trained personnel. Unfortunately, most countrywide censuses exercises are conducted only sporadically, making it difficult to track the short-term effect of policies to reduce poverty. This paper introduces a definition of vulnerability following UN-Habitat criteria, assesses different CNN machine learning architectures, and establishes a mapping between satellite images and survey data. Starting with the information corresponding to the 2,178,508 residential blocks recorded in the 2010 Mexican census and multispectral Landsat-7 images, multiple CNN architectures are explored. The best performance is obtained with EfficientNet-B3 achieving an area under the ROC and Precision-Recall curves of 0.9421 and 0.9457, respectively. This article shows that publicly available information, in the form of census data and satellite images, along with standard CNN architectures, may be employed as a stepping stone for the countrywide characterization of vulnerability at the residential block level. View Full-Text
Keywords: detecting and assessing vulnerability; satellite images and ground surveys; deep learning detecting and assessing vulnerability; satellite images and ground surveys; deep learning
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MDPI and ACS Style

Salas, J.; Vera, P.; Zea-Ortiz, M.; Villaseñor, E.-A.; Pulido, D.; Figueroa, A. Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning. Remote Sens. 2021, 13, 3603. https://doi.org/10.3390/rs13183603

AMA Style

Salas J, Vera P, Zea-Ortiz M, Villaseñor E-A, Pulido D, Figueroa A. Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning. Remote Sensing. 2021; 13(18):3603. https://doi.org/10.3390/rs13183603

Chicago/Turabian Style

Salas, Joaquín, Pablo Vera, Marivel Zea-Ortiz, Elio-Atenogenes Villaseñor, Dagoberto Pulido, and Alejandra Figueroa. 2021. "Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning" Remote Sensing 13, no. 18: 3603. https://doi.org/10.3390/rs13183603

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