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JDR Vol.18 No.4 pp. 379-387
(2023)
doi: 10.20965/jdr.2023.p0379

Paper:

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Revising the 2007 Peru Earthquake Damage Monitoring Using Machine Learning Models and Satellite Imagery

Bruno Adriano*1,† ORCID Icon, Hiroyuki Miura*2 ORCID Icon, Wen Liu*3, Masashi Matsuoka*4 ORCID Icon, Eduardo Portuguez*5, Miguel Diaz*6 ORCID Icon, and Miguel Estrada*6 ORCID Icon

*1International Research Institute of Disaster Science (IRIDeS), Tohoku University
468-1 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8572, Japan

†Corresponding author

*2Graduate School of Engineering, Hiroshima University
Higashi-hiroshima, Japan

*3Graduate School of Science and Engineering, Chiba University
Chiba, Japan

*4School of Environment and Society, Tokyo Institute of Technology
Yokohama, Japan

*5Engineering and Center for Estimation, Prevention, and Reduction of Disaster Risk (CENEPRED)
Lima, Peru

*6Japan Peru Center for Earthquake Engineering Research and Disaster Mitigation (CISMID), National University of Engineering (UNI)
Lima, Peru

Received:
January 8, 2023
Accepted:
April 7, 2023
Published:
June 1, 2023
Creative Commons License
Keywords:
building damage mapping, satellite imagery, deep learning, 2007 Peru earthquake
Abstract

We revised the building damage caused by the 2007 Pisco-Peru Earthquake using machine learning models and high-resolution satellite imagery. A framework for rapidly detecting collapsed buildings was proposed in the project “Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area” (JST-JICA SATREPS). The framework is based on a semantic segmentation model trained on freely available satellite and aerial imagery that does not include the target area. Thus, the generalization performance of the proposed framework was analyzed. Qualitative and quantitative analyses demonstrated that the proposed framework successfully predicted the distribution of collapsed buildings in Pisco. Moreover, it also reflects the ability to detect newly placed shelters. Our current trained model enables the rapid estimation of damaged buildings, crucial information for emergency response, and temporary refuges, which are also essential for fast rescue actions.

Cite this article as:
B. Adriano, H. Miura, W. Liu, M. Matsuoka, E. Portuguez, M. Diaz, and M. Estrada, “Revising the 2007 Peru Earthquake Damage Monitoring Using Machine Learning Models and Satellite Imagery,” J. Disaster Res., Vol.18 No.4, pp. 379-387, 2023.
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