{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T01:31:05Z","timestamp":1771637465133,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Inf Syst Front"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Issuing a disaster certificate, which is used to decide the contents of a victim\u2019s 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\u2009% accuracy and also detect whether a house is damaged, with 93.51\u2009% 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.<\/jats:p>","DOI":"10.1007\/s10796-021-10124-w","type":"journal-article","created":{"date-parts":[[2021,4,6]],"date-time":"2021-04-06T00:02:39Z","timestamp":1617667359000},"page":"351-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Estimation Method for Roof\u2010damaged Buildings\u00a0from Aero-Photo Images\u00a0During Earthquakes Using Deep Learning"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9610-5039","authenticated-orcid":false,"given":"Shono","family":"Fujita","sequence":"first","affiliation":[]},{"given":"Michinori","family":"Hatayama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"10124_CR1","unstructured":"Cabinet Office in Japan. (2013). Operation standard of damage certification for houses in disaster, pp. 1\u20133 (in Japanese). Available at: http:\/\/www.bousai.go.jp\/taisaku\/pdf\/shishinall.pdf. Accessed 18 Feb\u00a02020."},{"key":"10124_CR2","unstructured":"Cabinet Office in Japan. (2017). Operation manual of damage certification work for houses in disaster pp. 2, (in Japanese) Available at: http:\/\/www.bousai.go.jp\/taisaku\/pdf\/saigai_tebiki_full.pdf. Accessed 18 Feb\u00a02020."},{"key":"10124_CR3","unstructured":"Cabinet Office in Japan. (2018). Summary of revision in March 2018. (in Japanese) Available at: http:\/\/www.bousai.go.jp\/taisaku\/pdf\/h3003kaitei.pdf. Accessed 18 Feb\u00a02020."},{"key":"10124_CR4","unstructured":"Crisis Mappers Japan. (2020). Crisis Mappers Japan. (in Japanese) Available at: http:\/\/crisismappers.jp\/about.html. Accessed 1 Apr 2021."},{"key":"10124_CR5","doi-asserted-by":"crossref","unstructured":"Curran, K., Crumlish, J., & Fisher, G. (2012). OpenStreetMap. 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