{"_buckets": {"deposit": "6fcfc8a7-893e-4b4e-b07e-b8ec4f20514f"}, "_deposit": {"id": "3057", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "3057"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00003057", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2021-11", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "J2", "bibliographicPageEnd": "222", "bibliographicPageStart": "211", "bibliographicVolumeNumber": "2", "bibliographic_titles": [{"bibliographic_title": "AI・データサイエンス論文集", "bibliographic_titleLang": "ja"}, {"bibliographic_title": "Intelligence, Informatics and Infrastructure", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "災害発生直後の被害状況把握を目的に，ヘリコプターやドローン等により斜め方向から撮影された航空写真を用いた深層学習により，画像内の建物や斜面崩壊箇所を自動抽出するとともに，建物被害については無被害，損傷，倒壊にそれぞれ相当する3段階に自動判別するモデルを開発した．このモデルを用いて未学習のテスト用航空写真を用いた判別を行った結果，各区分におけるF値の平均値が約64%，mAPが約0.35の判別性能を確認した．", "subitem_description_language": "ja", "subitem_description_type": "Other"}, {"subitem_description": "For the purpose of damage detection immediately after a disaster, we developed a deep learning model using aerial photographs taken from an oblique direction with a helicopter or drone. This model automatically extracts damages to buildings and landslides, then divides into four classes: no damage, damage, collapse and landslide. As a result of discrimination using unlearned test aerial photographs using this model, it was confirmed that the average Fmeasure of each class was about 64% and mAP was about 0.35.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "公益社団法人 土木学会", "subitem_publisher_language": "ja"}, {"subitem_publisher": "Japan Society of Civil Engineers", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.11532/jsceiii.2.J2_211"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2435-9262", "subitem_source_identifier_type": "EISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "内藤 昌平", "creatorNameLang": "ja"}, {"creatorName": "Shohei NAITO", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "友澤 弘充", "creatorNameLang": "ja"}, {"creatorName": "Hiromitsu TOMOZAWA", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "森 悠史", "creatorNameLang": "ja"}, {"creatorName": "Yuji MORI", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "中村 洋光", "creatorNameLang": "ja"}, {"creatorName": "Hiromitsu NAKAMURA", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "藤原 広行", "creatorNameLang": "ja"}, {"creatorName": "Hiroyuki FUJIWARA", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_title": "斜め航空写真と深層学習を用いた建物被害抽出モデルの開発", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "斜め航空写真と深層学習を用いた建物被害抽出モデルの開発", "subitem_title_language": "ja"}, {"subitem_title": "DEVELOPMENT OF THE BUILDING DAMAGE DETECTION MODEL USING OBLIQUE AERIAL PHOTOGRAPHY AND DEEP LEARNING", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "1", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/3057", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "3057", "relation": {}, "relation_version_is_last": true, "title": ["斜め航空写真と深層学習を用いた建物被害抽出モデルの開発"], "weko_shared_id": -1}