{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:53:20Z","timestamp":1775066000169,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NATIONAL KEY R&amp;D PLAN OF China","award":["2022YFC3004404"],"award-info":[{"award-number":["2022YFC3004404"]}]},{"name":"NATIONAL KEY R&amp;D PLAN OF China","award":["2023YFF1305303"],"award-info":[{"award-number":["2023YFF1305303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method\u2019s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields.<\/jats:p>","DOI":"10.3390\/rs16071161","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T12:41:57Z","timestamp":1711543317000},"page":"1161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy"],"prefix":"10.3390","volume":"16","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"}]},{"given":"Yingjun","family":"Xu","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5720-2338","authenticated-orcid":false,"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8208-8373","authenticated-orcid":false,"given":"Jidong","family":"Wu","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jianhui","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Xiaoxuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}]},{"given":"Ruyi","family":"Peng","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101562","DOI":"10.1016\/j.uclim.2023.101562","article-title":"Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China","volume":"49","author":"Gao","year":"2023","journal-title":"Urban Clim."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15410","DOI":"10.1038\/s41598-023-42736-4","article-title":"Landslide risk evaluation method of open-pit mine based on numerical simulation of large deformation of landslide","volume":"13","author":"Jia","year":"2023","journal-title":"Sci. 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