{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:39:23Z","timestamp":1782952763201,"version":"3.54.5"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52208006"],"award-info":[{"award-number":["52208006"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["TICGP2023K002"],"award-info":[{"award-number":["TICGP2023K002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Innovation Center for Geological Disaster Prevention and Ecological Restoration in Western China, MNR (Chengdu University of Technology)","award":["52208006"],"award-info":[{"award-number":["52208006"]}]},{"name":"Technology Innovation Center for Geological Disaster Prevention and Ecological Restoration in Western China, MNR (Chengdu University of Technology)","award":["TICGP2023K002"],"award-info":[{"award-number":["TICGP2023K002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya\u2019an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models\u2019 resilience in an open-source landslide database. The results show that in the Ya\u2019an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.<\/jats:p>","DOI":"10.3390\/rs16173119","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"3119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion"],"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"},{"name":"Key Laboratory of Earth Exploration and Information Techniques, Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenghao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"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"}]},{"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":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, 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":"Jianhui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.gr.2024.02.006","article-title":"Landslide Spatial Prediction Using Cluster Analysis","volume":"130","author":"Zhao","year":"2024","journal-title":"Gondwana Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"GIS-Based Landslide Susceptibility Assessment Using Optimized Hybrid Machine Learning Methods","volume":"196","author":"Chen","year":"2021","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the Prediction Performance of a Deep Learning Neural Network Model with Conventional Machine Learning Models in Landslide Susceptibility Assessment","volume":"188","author":"Bui","year":"2020","journal-title":"Catena"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.gr.2022.05.012","article-title":"Machine Learning-Based Landslide Susceptibility Assessment with Optimized Ratio of Landslide to Non-Landslide Samples","volume":"123","author":"Yang","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cheng, G., Wang, Z., Huang, C., Yang, Y., Hu, J., Yan, X., Tan, Y., Liao, L., Zhou, X., and Li, Y. 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