{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:51:16Z","timestamp":1778788276072,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Technology Research and Development Program of Shandong Provincial Bureau of Geology and Mineral Resources (SDGM)","award":["KY202224"],"award-info":[{"award-number":["KY202224"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The effectiveness of landslide disaster prevention depends largely on the quality of early identification of potential hazards, and how to comprehensively, deeply, and accurately identify such hazards has become a major difficulty in landslide disaster management. Existing deep learning methods for potential landslide hazard identification often use fixed-size window modeling and ignore the different window sizes required by landslides of different scales. To address this problem, we propose an adaptive identification method for potential landslide hazards based on multisource data. Taking Yongping County, China, as the study area, we create a multisource factor dataset based on the landslide disaster background in terms of topography, geology, human activities, hydrology, and vegetation as the sample for the identification model after processing. Moreover, we combine differential interferometric synthetic aperture radar (D-InSAR) and multitemporal InSAR (MT-InSAR) to process the surface deformation of the study area, and we measure the deformation richness based on the average of the pixel deformation difference within the current window of a pixel point in the image. Therefore, convolutional neural networks (CNNs) with different window sizes are adaptively selected. The results show that the precision of adaptive identification of potential landslide hazards in the study area is 85.30%, the recall is 83.03%, and the F1 score is 84.15%. The recognition rate for potential hazards reaches 80%, which is better than the fixed-window modeling result and proves the effectiveness of the proposed method. This method can help to improve intelligent identification systems for potential landslide hazards, and also contribute to the identification of other potential geological hazards, such as mudslides and collapses.<\/jats:p>","DOI":"10.3390\/rs15071865","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T01:37:02Z","timestamp":1680226622000},"page":"1865","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Adaptive Identification Method for Potential Landslide Hazards Based on Multisource Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenping","family":"Yin","sequence":"first","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqing","family":"Bai","sequence":"additional","affiliation":[{"name":"Shandong Province Institute of Land Surveying and Mapping, Jinan 250013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deqiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Shandong GEO-Surveying & Mapping Institute, Jinan 250002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-0313","authenticated-orcid":false,"given":"Xiran","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Computing and Mathematics, College of Science and Engineering, University of Derby, Derby DE22 1GB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s11069-021-04650-8","article-title":"Automated landslide detection model to delineate the extent of existing landslides","volume":"107","author":"Alimohammadlou","year":"2021","journal-title":"Nat. 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