Estimation Method for Roof‐damaged Buildings from Aero-Photo Images During Earthquakes Using Deep Learning

Shono Fujita () and Michinori Hatayama ()
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Shono Fujita: Kyoto University
Michinori Hatayama: Kyoto University

Information Systems Frontiers, 2023, vol. 25, issue 1, No 19, 363 pages

Abstract: Abstract Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake.

Keywords: Damage certification; Deep learning; Image recognition; Aero photo; GIS (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-021-10124-w

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