{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:32:20Z","timestamp":1771065140319,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071407"],"award-info":[{"award-number":["42071407"]}]},{"name":"National Natural Science Foundation of China","award":["E3Z219010F"],"award-info":[{"award-number":["E3Z219010F"]}]},{"name":"National Natural Science Foundation of China","award":["2023PT001"],"award-info":[{"award-number":["2023PT001"]}]},{"name":"Science and Disruptive Technology Research Pilot Fund of the Aerospace Information Innovation Institute, the Chinese Academy of Sciences","award":["42071407"],"award-info":[{"award-number":["42071407"]}]},{"name":"Science and Disruptive Technology Research Pilot Fund of the Aerospace Information Innovation Institute, the Chinese Academy of Sciences","award":["E3Z219010F"],"award-info":[{"award-number":["E3Z219010F"]}]},{"name":"Science and Disruptive Technology Research Pilot Fund of the Aerospace Information Innovation Institute, the Chinese Academy of Sciences","award":["2023PT001"],"award-info":[{"award-number":["2023PT001"]}]},{"name":"Open Fund of Key Laboratory of Urban Spatial Information, Ministry of Natural Resources","award":["42071407"],"award-info":[{"award-number":["42071407"]}]},{"name":"Open Fund of Key Laboratory of Urban Spatial Information, Ministry of Natural Resources","award":["E3Z219010F"],"award-info":[{"award-number":["E3Z219010F"]}]},{"name":"Open Fund of Key Laboratory of Urban Spatial Information, Ministry of Natural Resources","award":["2023PT001"],"award-info":[{"award-number":["2023PT001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land-cover mapping plays a crucial role in resource detection, ecological environmental protection, and sustainable development planning. The existing large-scale land-cover products with coarse spatial resolution have a wide range of categories, but they suffer from low mapping accuracy. Conversely, land-cover products with fine spatial resolution tend to lack diversity in the types of land cover they encompass. Currently, there is a lack of large-scale land-cover products simultaneously possessing fine-grained classifications and high accuracy. Therefore, we propose a mapping framework for fine-grained land-cover classification. Firstly, we propose an iterative method for developing fine-grained classification systems, establishing a classification system suitable for Sentinel-2 data based on the target area. This system comprises 23 fine-grained land-cover types and achieves the most stable mapping results. Secondly, to address the challenges in large-scale scenes, such as varying scales of target features, imbalanced sample quantities, and the weak connectivity of slender features, we propose an improved network based on Swin-UNet. This network incorporates a pyramid pooling module and a weighted combination loss function based on class balance. Additionally, we independently trained models for roads and water. Guided by the natural spatial relationships, we used a voting algorithm to integrate predictions from these independent models with the full classification model. Based on this framework, we created the 2017 Beijing\u2013Tianjin\u2013Hebei regional fine-grained land-cover product JJJLC-10. Through validation using 4254 sample datasets, the results indicate that JJJLC-10 achieves an overall accuracy of 80.3% in the I-level validation system (covering seven land-cover types) and 72.2% in the II-level validation system (covering 23 land-cover types), with kappa coefficients of 0.7602 and 0.706, respectively. In comparison with widely used land-cover products, JJJLC-10 excels in accurately depicting the spatial distribution of various land-cover types and exhibits significant advantages in terms of classification quantity and accuracy.<\/jats:p>","DOI":"10.3390\/rs16020390","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T11:28:46Z","timestamp":1705577326000},"page":"390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Framework for Fine-Grained Land-Cover Classification Using 10 m Sentinel-2 Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Wenge","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2938-7419","authenticated-orcid":false,"given":"Xuan","family":"Yang","sequence":"additional","affiliation":[{"name":"China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhanliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-6459","authenticated-orcid":false,"given":"Zhengchao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-2107","authenticated-orcid":false,"given":"Yue","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying urban land use by integrating remote sensing and social media data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. 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