{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:29:35Z","timestamp":1782390575933,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52208006"],"award-info":[{"award-number":["52208006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model.<\/jats:p>","DOI":"10.3390\/rs16020347","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T11:15:01Z","timestamp":1705317301000},"page":"347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Li","sequence":"additional","affiliation":[{"name":"Mahindra United World College of India, Pune 412108, MH, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengmeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sizhi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Sichuan Earthquake Agency, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoda","family":"Li","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6756-2999","authenticated-orcid":false,"given":"Luting","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiegang","family":"Sun","sequence":"additional","affiliation":[{"name":"China Building Materials Southwest Survey and Design Co., Ltd., Chengdu 610052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-8801","authenticated-orcid":false,"given":"Weile","family":"Li","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4653-4277","authenticated-orcid":false,"given":"Peilian","family":"Ran","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojie","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangzhou Hi-Target Navigation Tech Co., Ltd., Guangzhou 511400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhao","sequence":"additional","affiliation":[{"name":"ANT Intelligence Service (Chengdu) Information Technology Co., Ltd., Chengdu 610040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Mianyang Polytechnic, Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104777","DOI":"10.1016\/j.catena.2020.104777","article-title":"GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models","volume":"195","author":"Chen","year":"2020","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoen coder neural network for landslide susceptibility 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