{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:12:31Z","timestamp":1760145151282,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["41901279","K202239","CX2023312"],"award-info":[{"award-number":["41901279","K202239","CX2023312"]}]},{"name":"the Science Foundation Research Project of Wuhan Institute of Technology of China","award":["41901279","K202239","CX2023312"],"award-info":[{"award-number":["41901279","K202239","CX2023312"]}]},{"name":"the Graduate Innovative Fund of Wuhan Institute of Technology","award":["41901279","K202239","CX2023312"],"award-info":[{"award-number":["41901279","K202239","CX2023312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17\u201383.29% and 39.55\u201375.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16\u201334.27% and 8.32\u201343.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method.<\/jats:p>","DOI":"10.3390\/rs16122248","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T11:42:03Z","timestamp":1718883723000},"page":"2248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generating 10-Meter Resolution Land Use and Land Cover Products Using Historical Landsat Archive Based on Super Resolution Guided Semantic Segmentation Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Dawei","family":"Wen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Shihao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Systems Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xuehua","family":"Guan","sequence":"additional","affiliation":[{"name":"China Siwei Surveying and Mapping Technology Co., Ltd., Beijing 100044, China"}]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.jum.2018.11.002","article-title":"Quantification of the land use\/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone","volume":"8","author":"Mohamed","year":"2019","journal-title":"J. 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