{"_buckets": {"deposit": "d634df69-3a01-417d-8178-acb910b871aa"}, "_deposit": {"created_by": 7, "id": "7286", "owners": [7], "pid": {"revision_id": 0, "type": "depid", "value": "7286"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00007286", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2025-07-30", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "15", "bibliographicPageEnd": "2638", "bibliographicPageStart": "2638", "bibliographicVolumeNumber": "17", "bibliographic_titles": [{"bibliographic_title": "Remote Sensing", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "MDPI AG", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.3390/rs17152638"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "2072-4292", "subitem_source_identifier_type": "EISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Satomi Kimijima", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Chun Ping", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shono Fujita", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Makoto Hanashima", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Shingo Toride", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Hitoshi Taguchi", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_title": "Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "7", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/7286", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2025-08-18"}, "publish_date": "2025-08-18", "publish_status": "0", "recid": "7286", "relation": {}, "relation_version_is_last": true, "title": ["Stepwise Building Damage Estimation Through Time-Scaled Multi-Sensor Integration: A Case Study of the 2024 Noto Peninsula Earthquake"], "weko_shared_id": -1}