{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:17:06Z","timestamp":1783700226464,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801323"],"award-info":[{"award-number":["41801323"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M682038"],"award-info":[{"award-number":["2020M682038"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Innovation Committee","award":["ZDSYS20170725140921348"],"award-info":[{"award-number":["ZDSYS20170725140921348"]}]},{"name":"Shenzhen Science and Technology Innovation Committee","award":["JCYJ20190813170803617"],"award-info":[{"award-number":["JCYJ20190813170803617"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. (1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. (2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. (3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13040548","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaokang","family":"Zhang","sequence":"first","affiliation":[{"name":"Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"CUHK(SZ)-CAS-NOVA Joint Laboratory, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"School of Mathematical Sciences, University of Science and Technology of China, Hefei 230026, China"},{"name":"Shenzhen Research Institute of Big Data, Shenzhen 518172, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3316-5381","authenticated-orcid":false,"given":"Man-On","family":"Pun","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"CUHK(SZ)-CAS-NOVA Joint Laboratory, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"Shenzhen Research Institute of Big Data, Shenzhen 518172, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"CUHK(SZ)-CAS-NOVA Joint Laboratory, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"Shanghai CAS-NOVA Satellite Technology Company Limited, Shanghai 201210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/01431160701227661","article-title":"Quantitative assessment of landslide susceptibility using high-resolution remote sensing data and a generalized additive model","volume":"29","author":"Park","year":"2008","journal-title":"Int. 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