@article{oai:ipsj.ixsq.nii.ac.jp:00218206,
 author = {藤田, 翔乃 and 畑山, 満則 and Shono, Fujita and Michinori, Hatayama},
 issue = {2},
 journal = {情報処理学会論文誌コンシューマ・デバイス＆システム（CDS）},
 month = {May},
 note = {災害が起こった際，自治体は被災した住家の被害の程度を調査し，被災者に罹災証明書を交付する．罹災証明書は被災者の支援策の判断材料として活用され，生活再建に必要不可欠であるため，自治体は迅速かつ正確に発行しなければならない．しかし，これまでの地震災害では被害認定調査・罹災証明書発行に多くの時間を要しており，円滑な被災者支援を妨げていた．加えて現在の屋根調査においては，屋根すべてを見渡すことができず，正確に屋根調査を行えていない．そこで，本研究では航空写真から自動で屋根損傷率を算出する画像処理モデルを開発した．筆者らの先行研究(2020)から得た学習データの不足という問題に対して，本研究は深層学習による画像セグメンテーションを用いて屋根画像を屋根面で分割して学習データを増加させるという方法をとった．, In the event of a natural disaster, Japanese local governments investigate the level of damage of the buildings and issue damage certificates to the victims. The damage certificate is used to determine the contents of support provided to the victims; hence, they must be issued rapidly and accurately. However, in the past, the investigation of damage was time consuming, thus delaying the support provided to the victims. Additionally, while investigating the roof of the damaged building, it was difficult for the investigators to look at the entire roof and calculate the damage rate accurately. To address this issue, we have developed an image processing model to automatically calculate the rate of damage on a roof through image recognition from aerial photos. To circumvent the problem of lack of training data reported in our previous study (2020), in this study, roof images were divided into roof surfaces based on image segmentation by deep learning, and the number of training data was increased.},
 pages = {12--26},
 title = {画像セグメンテーションを用いた屋根面分割による屋根損傷率自動算出手法の開発},
 volume = {12},
 year = {2022}
}