{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:25:12Z","timestamp":1771478712345,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030101"],"award-info":[{"award-number":["XDA19030101"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2022122"],"award-info":[{"award-number":["2022122"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["guikeAA20302022"],"award-info":[{"award-number":["guikeAA20302022"]}]},{"name":"Youth Innovation Promotion Association","award":["XDA19030101"],"award-info":[{"award-number":["XDA19030101"]}]},{"name":"Youth Innovation Promotion Association","award":["2022122"],"award-info":[{"award-number":["2022122"]}]},{"name":"Youth Innovation Promotion Association","award":["guikeAA20302022"],"award-info":[{"award-number":["guikeAA20302022"]}]},{"name":"China-ASEAN Big Earth Data Platform and Applications","award":["XDA19030101"],"award-info":[{"award-number":["XDA19030101"]}]},{"name":"China-ASEAN Big Earth Data Platform and Applications","award":["2022122"],"award-info":[{"award-number":["2022122"]}]},{"name":"China-ASEAN Big Earth Data Platform and Applications","award":["guikeAA20302022"],"award-info":[{"award-number":["guikeAA20302022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately detecting landslides over a large area with complex background objects is a challenging task. Research in the area suffers from three drawbacks in general. First, the models are mostly modified from typical networks, and are not designed specifically for landslide detection. Second, the images used to construct and evaluate models of landslide detection are limited to one spatial resolution, which struggles to meet the requirements of such relevant applications as emergency response. Third, assessments are primarily carried out by using the training data on different parts of the same study area. This makes it difficult to objectively evaluate the transferability of the model, because ground objects in the same area are distributed with similar spectral characteristics. To respond to the challenges above, this study proposes DeenNet, specifically designed for landslide detection. Different from the widely used encoder\u2013decoder networks, DeenNet maintains multi-scale landslide features by decoding the input feature maps to a large scale before encoding a module. The decoding operation is conducted by deconvolution of the input feature maps, while encoding is conducted by convolution. Our model is trained on two earthquake-triggered landslide datasets, constructed using images with different spatial resolutions from different sensor platforms. Two other landslide datasets of different study areas with different spatial resolutions were used to evaluate the trained model. The experimental results demonstrated an at least 6.17% F1-measure improvement by DeenNet compared with three widely used typical encoder\u2013decoder-based networks. The decoder\u2013encoder network structure of DeenNet proves to be effective in maintaining landslide features, regardless of the size of the landslides in different evaluation images. It further validated the capacity of DeenNet in maintaining landslide features, which provides a strong applicability in the context of applications.<\/jats:p>","DOI":"10.3390\/rs14225759","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Network for Landslide Detection Using Large-Area Remote Sensing Images with Multiple Spatial Resolutions"],"prefix":"10.3390","volume":"14","author":[{"given":"Bo","family":"Yu","sequence":"first","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3956-4925","authenticated-orcid":false,"given":"Chong","family":"Xu","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing100085, China"}]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-3644","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s10346-019-01167-x","article-title":"The cost of rapid and haphazard urbanization: Lessons learned from the Freetown landslide disaster","volume":"16","author":"Cui","year":"2019","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e3998","DOI":"10.1002\/ett.3998","article-title":"Review on remote sensing methods for landslide detection using machine and deep learning","volume":"32","author":"Mohan","year":"2020","journal-title":"Trans. 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