{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:47:05Z","timestamp":1762253225735,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of \u201cone view per city\u201d is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.<\/jats:p>","DOI":"10.3390\/s20010141","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T10:28:43Z","timestamp":1577183323000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study"],"prefix":"10.3390","volume":"20","author":[{"given":"Jianguang","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"},{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China"},{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0464-9862","authenticated-orcid":false,"given":"Cong","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"},{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Ocean College, Minjiang University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Technology, Beijing Normal University, Zhuhai 519087, China"},{"name":"Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02247-0","article-title":"Remote sensing image processing","volume":"5","author":"Tuia","year":"2011","journal-title":"Synth. 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