{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:36:03Z","timestamp":1773282963503,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent decades, land use\/cover change (LUCC) due to urbanization, deforestation, and desertification has dramatically increased, which changes the global landscape and increases the pressure on the environment. LUCC not only accelerates global warming but also causes widespread and irreversible loss of biodiversity. Therefore, LUCC reconstruction has important scientific and practical value for studying environmental and ecological changes. The commonly used LUCC reconstruction models can no longer meet the growing demand for uniform and high-resolution LUCC reconstructions. In view of this circumstance, a deep learning-integrated LUCC reconstruction model (DLURM) was developed in this study. Zhenlai County of Jilin Province (1986\u20132013) was taken as an example to verify the proposed DLURM. The average accuracy of the DLURM reached 92.87% (compared with the results of manual interpretation). Compared with the results of traditional models, the DLURM had significantly better accuracy and robustness. In addition, the simulation results generated by the DLURM could match the actual land use (LU) map better than those generated by other models.<\/jats:p>","DOI":"10.3390\/rs12203314","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Model Integrating Deep Learning for Land Use\/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3531-9498","authenticated-orcid":false,"given":"Zhang","family":"Yubo","sequence":"first","affiliation":[{"name":"College of Earth Sciences, Jilin University, Changchun 130021, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhuoran","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Jilin University, Changchun 130021, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Jiuchun","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1080-0135","authenticated-orcid":false,"given":"Yang","family":"Yuanyuan","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Dongyan","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Jilin University, Changchun 130021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Yucong","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Fengqin","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5565-535X","authenticated-orcid":false,"given":"Yu","family":"Lingxue","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Liping","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Shuwen","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land. use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. 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