{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:36:09Z","timestamp":1774928169006,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFC3004404"],"award-info":[{"award-number":["2022YFC3004404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA.<\/jats:p>","DOI":"10.3390\/rs16214006","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T09:51:26Z","timestamp":1730109086000},"page":"4006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic Earthquake-Induced Landslide Susceptibility Assessment Model: Integrating Machine Learning and Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"given":"Youtian","family":"Yang","sequence":"first","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"},{"name":"School of System Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8208-8373","authenticated-orcid":false,"given":"Jidong","family":"Wu","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Plateau Science and Sustainability, People\u2019s Government of Qinghai Province and Beijing Normal University, Xining 810008, China"}]},{"given":"Lili","family":"Wang","sequence":"additional","affiliation":[{"name":"Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, China"},{"name":"School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875, China"},{"name":"School of System Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Ru","family":"Ya","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Rumei","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.enggeo.2018.05.004","article-title":"What We Have Learned from the 2008 Wenchuan Earthquake and Its Aftermath: A Decade of Research and Challenges","volume":"241","author":"Fan","year":"2018","journal-title":"Eng. 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