Detection of Location from Kits Set Up by Vulnerable People during Earthquake Disasters with Communication Blackout: Study Using YOLOv5 Algorithm

Yuma Morisaki, Makoto Fujiu (), Taiki Suwa, Ryoichi Furuta and Junichi Takayama
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Yuma Morisaki: Faculty of Transdisciplinary Sciences for Innovation, Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa 920-1192, Japan
Makoto Fujiu: Faculty of Transdisciplinary Sciences for Innovation, Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa 920-1192, Japan
Taiki Suwa: Division of Environmental Design, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan
Ryoichi Furuta: Remote Sensing Technology Center of Japan, Minato 105-0001, Japan
Junichi Takayama: Graduate School of Sustainable Systems Science, Komatsu University, Komatsu 923-8511, Japan

Sustainability, 2022, vol. 14, issue 21, 1-13

Abstract: When an earthquake occurs, the larger the scale of the disaster, the harder it is to support the victims’ needs. In fact, even the most meager support for victims has become quite difficult. Furthermore, it is also known that the greater the damage, the more difficult it becomes to use cell phones, applications, etc. The authors have developed multiple reflectors observable by synthetic-aperture radar (SAR) satellites and differing backscattering coefficients. Using them, we have proposed a method for ascertaining the location and needs of victims during a large-scale earthquake disaster. In this study, we developed an object detection model using YOLOv5 to detect the reflectors from within SAR images. In addition, we constructed a method for managing setup locations in GIS by conferring latitude and longitude information on reflectors obtained through YOLO v5. Through analysis, a model of the proposed reflector detection and identification of setup locations via GIS was developed in this study.

Keywords: vulnerable people; large-scale earthquake disaster; ascertaining location and needs; deep learning; satellite SAR images (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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