{"created":"2023-03-31T02:48:04.121651+00:00","id":5954,"links":{},"metadata":{"_buckets":{"deposit":"c8b16072-d346-4a54-8795-0e57517cc0c2"},"_deposit":{"id":"5954","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"5954"},"status":"published"},"_oai":{"id":"oai:nied-repo.bosai.go.jp:00005954","sets":[]},"author_link":[],"item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2016","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"3_146","bibliographicPageStart":"3_137","bibliographicVolumeNumber":"16","bibliographic_titles":[{"bibliographic_title":"日本地震工学会論文集","bibliographic_titleLang":"ja"},{"bibliographic_title":"Journal of Japan Association for Earthquake Engineering","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"想定地震による被害予測を行うためには，建物インベントリデータが必要である．しかし，発展途上国ではデータが整備されていない地域が多く，現地踏査によるデータの構築には多くの労力を伴う．一方，リモートセンシングは現地に行かずに広域を観測できる利点があり，近年地上分解能が1m以下の光学衛星が多く打ち上げられている．これにより，建物を詳細に把握できる高解像度衛星画像が取得できるようになった．本研究では，地震国ペルーにおいてタクナ市を対象地域として，WorldView-2衛星画像のオブジェクト分類を用いた建物インベントリの構築手法を検討した．","subitem_description_language":"ja","subitem_description_type":"Other"},{"subitem_description":"In conducting damage assessment for scenario earthquakes in high seismic risk regions, building inventory data are required as well as building fragility functions and strong-motion distributions. But inventory data with the locations and characteristics of buildings are not so easy to construct, especially for developing countries. Hence in this study, an approach to construct building inventory data is sought as an alternative of cadastral data and field surveys. Using a high-resolution optical satellite image acquired by WorldView-2, this paper tries to develop building inventory data for earthquake damage assessment in Tacna, Peru. First, Pixel-based classification was carried out to examine basic land-cover and land-use of the urban area. Object-based building extraction was then conducted for three selected areas as an attempt to develop building inventory data.","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 日本地震工学会","subitem_publisher_language":"ja"},{"subitem_publisher":"JAPAN ASSOCIATION FOR EARTHQUAKE ENGINEERING","subitem_publisher_language":"en"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.5610/jaee.16.3_137"}}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木 賢太郎","creatorNameLang":"ja"},{"creatorName":"SUZUKI Kentaro","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"リュウ ウェン","creatorNameLang":"ja"},{"creatorName":"LIU Wen","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"松岡 昌志","creatorNameLang":"ja"},{"creatorName":"MATSUOKA Masashi","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"山崎 文雄","creatorNameLang":"ja"},{"creatorName":"YAMAZAKI Fumio","creatorNameLang":"en"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_title":"衛星画像を用いたペルー・タクナ市における地震被害想定のための建物インベントリデータ構築に向けた試み","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"衛星画像を用いたペルー・タクナ市における地震被害想定のための建物インベントリデータ構築に向けた試み","subitem_title_language":"ja"},{"subitem_title":"Development of Building Inventory Data for Earthquake Damage Assessment in Tacna, Peru Using Satellite Imagery","subitem_title_language":"en"}]},"item_type_id":"40001","owner":"1","path":["1670839190650"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-03-30"},"publish_date":"2023-03-30","publish_status":"0","recid":"5954","relation_version_is_last":true,"title":["衛星画像を用いたペルー・タクナ市における地震被害想定のための建物インベントリデータ構築に向けた試み"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-06-08T09:33:23.914848+00:00"}