{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T19:51:29Z","timestamp":1773863489186,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Funds for National Science Foundation for Outstanding Young Scholars","doi-asserted-by":"publisher","award":["42125702"],"award-info":[{"award-number":["42125702"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Funds for National Science Foundation for Outstanding Young Scholars","doi-asserted-by":"publisher","award":["41521002"],"award-info":[{"award-number":["41521002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Funds for National Science Foundation for Outstanding Young Scholars","doi-asserted-by":"publisher","award":["2022NSFSC0003"],"award-info":[{"award-number":["2022NSFSC0003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Funds for National Science Foundation for Outstanding Young Scholars","doi-asserted-by":"publisher","award":["2022NSFSC1083"],"award-info":[{"award-number":["2022NSFSC1083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funds for Creative Research Groups of China","award":["42125702"],"award-info":[{"award-number":["42125702"]}]},{"name":"Funds for Creative Research Groups of China","award":["41521002"],"award-info":[{"award-number":["41521002"]}]},{"name":"Funds for Creative Research Groups of China","award":["2022NSFSC0003"],"award-info":[{"award-number":["2022NSFSC0003"]}]},{"name":"Funds for Creative Research Groups of China","award":["2022NSFSC1083"],"award-info":[{"award-number":["2022NSFSC1083"]}]},{"name":"The Natural Science Foundation of Sichuan Province","award":["42125702"],"award-info":[{"award-number":["42125702"]}]},{"name":"The Natural Science Foundation of Sichuan Province","award":["41521002"],"award-info":[{"award-number":["41521002"]}]},{"name":"The Natural Science Foundation of Sichuan Province","award":["2022NSFSC0003"],"award-info":[{"award-number":["2022NSFSC0003"]}]},{"name":"The Natural Science Foundation of Sichuan Province","award":["2022NSFSC1083"],"award-info":[{"award-number":["2022NSFSC1083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence\u2014aided recognition of these surface processes. However, so far, the technological advancements have not produced robust automated mapping tools whose domain of validity holds in any area across the globe. For instance, capturing historical landslides in densely vegetated areas is still a challenge. This study proposed a deep learning method based on Light Detection and Ranging (LiDAR) data for automatic identification of historical landslides. Additionally, it tested this method in the Jiuzhaigou earthquake-hit region of Sichuan Province (China). Specifically, we generated a Red Relief Image Map (RRIM), which was obtained via high-precision airborne LiDAR data, and on the basis of this information we trained a Lightweight Attention U-Net (LAU-Net) to map a total of 1949 historical landslides. Overall, our model recognized the aforementioned landslides with high accuracy and relatively low computational costs. We compared multiple performance indexes across several deep learning routines and different data types. The results showed that the Multiple-Class based Semantic Image Segmentation (MIOU) and the F1_score of the LAU-Net and RRIM reached 82.29% and 87.45%, which represented the best performance among the methods we tested.<\/jats:p>","DOI":"10.3390\/rs14174357","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net"],"prefix":"10.3390","volume":"14","author":[{"given":"Chengyong","family":"Fang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xuanmei","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Hao","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-7288","authenticated-orcid":false,"given":"Luigi","family":"Lombardo","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"given":"Hakan","family":"Tanyas","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6653-2585","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sassa, K., and Canuti, P. 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