{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:26:48Z","timestamp":1774880808585,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2017YFC1500902"],"award-info":[{"award-number":["2017YFC1500902"]}]},{"name":"The Second Tibetan Plateau Scientific Expedition and Research (STEP\uff09","award":["2019QZKK0806"],"award-info":[{"award-number":["2019QZKK0806"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.<\/jats:p>","DOI":"10.3390\/rs12050894","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T11:59:36Z","timestamp":1583841576000},"page":"894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model"],"prefix":"10.3390","volume":"12","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yongming","family":"Wei","sequence":"additional","affiliation":[{"name":"National Engineering Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100101, China"}]},{"given":"Qinjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of the Earth Observation of Hainan Province, Aerospace Information Research Institute, CAS, Hainan Research Institute, Sanya 572029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-243X","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"}]},{"given":"Jingjing","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10346-009-0148-5","article-title":"Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China","volume":"6","author":"Yin","year":"2009","journal-title":"Landslides"},{"key":"ref_2","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. 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