{"_buckets": {"deposit": "033c9948-0299-4d92-8e21-90b5df55f5d5"}, "_deposit": {"created_by": 7, "id": "7297", "owners": [7], "pid": {"revision_id": 0, "type": "depid", "value": "7297"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00007297", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2025-08-20", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{"bibliographic_title": "Research Square(preprints)", "bibliographic_titleLang": "ja"}, {"bibliographic_title": "Research Square(preprints)", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Abstract \u003cp\u003eQuickly and accurately evaluating building damage after a major earthquake is essential for effective emergency response. We propose a practical framework that integrates camera-equipped ground vehicles deployed by multiple agencies (such as the Self-Defense Forces, police, and fire services) with a centralized command system to perform real-time post-earthquake damage mapping. The system combines an RGB-based damage detection technique (gNCDI), which generalises the simple Redness Index (RI) originally developed for vegetation analysis, with a Genetic Algorithm (GA) to optimise the patrol routes of multiple vehicles. Using colour-based inference, collapsed buildings are rapidly identified from ground-level images by detecting the spectral signatures of exposed timber and soil debris, while the GA efficiently allocates routes to each vehicle to maximise coverage and minimise response time. A cloud-based architecture standardises and shares geotagged damage reports in real time using a JSON format across all responding agencies. We present the system design, implementation details, and evaluation protocol based on a disaster scenario simulation for the Noto Peninsula region in Japan. In our evaluation, the proposed approach achieved a high overall classification accuracy (F1 score ≈ 0.86), detecting 90% of collapsed buildings with only ~ 18% false alarms. At the same time, the cooperative vehicle-routing strategy significantly improved survey efficiency, shortening total mission completion time by around 25% compared to a greedy baseline. Furthermore, we discuss practical issues including the speed and resolution advantages over traditional satellite or aerial assessments, data privacy considerations, false detections, and the need for human verification of results. Overall, this study demonstrates a feasible multi-vehicle, multi-agency approach for rapid earthquake damage estimation aimed at accelerating life-saving rescue operations and optimising resource allocation.\u003c/p\u003e", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Springer Science and Business Media LLC", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.21203/rs.3.rs-7390282/v1"}}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Haruhiro Shiraishi", "creatorNameLang": "ja"}, {"creatorName": "Haruhiro Shiraishi", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Yuichiro Usuda", "creatorNameLang": "ja"}, {"creatorName": "Yuichiro Usuda", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_title": "A Practical Framework for Rapid Earthquake Damage Estimation through Multi-Vehicle Coordination and Inter-Agency Collaboration: Integrating Genetic Algorithms with RGB-Based Image Analysis", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "A Practical Framework for Rapid Earthquake Damage Estimation through Multi-Vehicle Coordination and Inter-Agency Collaboration: Integrating Genetic Algorithms with RGB-Based Image Analysis", "subitem_title_language": "ja"}, {"subitem_title": "A Practical Framework for Rapid Earthquake Damage Estimation through Multi-Vehicle Coordination and Inter-Agency Collaboration: Integrating Genetic Algorithms with RGB-Based Image Analysis", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "7", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/7297", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2025-09-01"}, "publish_date": "2025-09-01", "publish_status": "0", "recid": "7297", "relation": {}, "relation_version_is_last": true, "title": ["A Practical Framework for Rapid Earthquake Damage Estimation through Multi-Vehicle Coordination and Inter-Agency Collaboration: Integrating Genetic Algorithms with RGB-Based Image Analysis"], "weko_shared_id": -1}