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Open AccessArticle

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

1
Information Technology Group, Wageningen University & Research, 6700 EW Wageningen, The Netherlands
2
Business Economics Group, Wageningen University & Research, 6700 EW Wageningen, The Netherlands
3
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Rajib Shaw
Climate 2021, 9(4), 58; https://doi.org/10.3390/cli9040058
Received: 14 March 2021 / Revised: 1 April 2021 / Accepted: 3 April 2021 / Published: 6 April 2021
(This article belongs to the Special Issue Climate Change, Sustainable Development and Disaster Risks)
Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level. View Full-Text
Keywords: deprived areas; slums; disaster; recovery; damage; remote sensing; machine learning; SVM; SDG; Sendai Framework deprived areas; slums; disaster; recovery; damage; remote sensing; machine learning; SVM; SDG; Sendai Framework
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MDPI and ACS Style

Ghaffarian, S.; Emtehani, S. Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery. Climate 2021, 9, 58. https://doi.org/10.3390/cli9040058

AMA Style

Ghaffarian S, Emtehani S. Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery. Climate. 2021; 9(4):58. https://doi.org/10.3390/cli9040058

Chicago/Turabian Style

Ghaffarian, Saman; Emtehani, Sobhan. 2021. "Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery" Climate 9, no. 4: 58. https://doi.org/10.3390/cli9040058

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