{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:04:54Z","timestamp":1781107494325,"version":"3.54.1"},"reference-count":11,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100011171","name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection","doi-asserted-by":"publisher","award":["SKLGP2022K026"],"award-info":[{"award-number":["SKLGP2022K026"]}],"id":[{"id":"10.13039\/501100011171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006385","name":"Chengdu University of Technology","doi-asserted-by":"publisher","award":["SKLGP2022K026"],"award-info":[{"award-number":["SKLGP2022K026"]}],"id":[{"id":"10.13039\/501100006385","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,15]]},"DOI":"10.1109\/geoinformatics57846.2022.9963885","type":"proceedings-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T21:05:43Z","timestamp":1670015143000},"page":"1-4","source":"Crossref","is-referenced-by-count":11,"title":["Landslide Detection Methods Based on Deep Learning in Remote Sensing Images"],"prefix":"10.1109","author":[{"given":"Lei","family":"Wu","sequence":"first","affiliation":[{"name":"Chengdu University of Technology,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gulin","family":"Li","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsong","family":"Gou","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhu","family":"Lei","sequence":"additional","affiliation":[{"name":"Chengdu University of Technology,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref4","first-page":"40","article-title":"A landslide intelligent detection method based on CNN and RSG_R[C]","author":"h","year":"2017","journal-title":"2017 IEEE International Conference on Mechatronics and Automation (lCMA)"},{"key":"ref3","first-page":"1747","article-title":"Automatic Object Detection of Loess Landslide Based on Deep Learning[J]","volume":"45","author":"ju","year":"2020","journal-title":"Geomatics and Information Science of Wuhan university"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref11","author":"liu","year":"2016","journal-title":"Ssd Single shot multibox detector"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/rs12010044"},{"key":"ref8","article-title":"YOLOv3: An Incremental Improvement[J]","author":"redmon","year":"2018","journal-title":"ArXiv e-prints"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref2","first-page":"26","article-title":"Fast Interpretation Methods of Landslides Triggered by Earthquake Using Remote Sensing Imagery[J]","volume":"4","author":"li","year":"2015","journal-title":"Remote Sensing Information"},{"key":"ref9","author":"erhan","year":"0","journal-title":"Scalable object detection using deep neural networks In CVPR"},{"key":"ref1","first-page":"1000","article-title":"Large-scale landslides and their sliding mechanisms in China since the 20th century [J]","author":"huang","year":"2007","journal-title":"Chinese Journal of Rock Mechanics and Engineering"}],"event":{"name":"2022 29th International Conference on Geoinformatics","location":"Beijing, China","start":{"date-parts":[[2022,8,15]]},"end":{"date-parts":[[2022,8,18]]}},"container-title":["2022 29th International Conference on Geoinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9963788\/9963792\/09963885.pdf?arnumber=9963885","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T19:56:11Z","timestamp":1671479771000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9963885\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,15]]},"references-count":11,"URL":"https:\/\/doi.org\/10.1109\/geoinformatics57846.2022.9963885","relation":{},"subject":[],"published":{"date-parts":[[2022,8,15]]}}}