{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:19:55Z","timestamp":1760145595741,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"],"award-info":[{"award-number":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018617","name":"Liaoning Revitalization Talents Program","doi-asserted-by":"publisher","award":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"],"award-info":[{"award-number":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Applied Research Foundation of Liaoning Province","award":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"],"award-info":[{"award-number":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"]}]},{"name":"Sponsored by Beijing Nova Program","award":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"],"award-info":[{"award-number":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"]}]},{"name":"Huzhou Key Research and Development Program","award":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"],"award-info":[{"award-number":["41871379","XLYC2007026","2022JH2\/101300273","2022JH2\/101300257","20230484351","2023ZD2046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Feature line extraction for building roofs is a critical step in the 3D model reconstruction of buildings. A feature line extraction algorithm for building roof point clouds based on the linear distribution characteristics of neighborhood points was proposed in this study. First, the virtual grid was utilized to provide local neighborhood information for the point clouds, aiding in identifying the linear distribution characteristics of the center of the gravity points on the feature line and determining the potential feature point set in the original point clouds. Next, initial segment elements were selected from the feature point set, and the iterative growth of these initial segment elements was performed by combining the RANSAC linear fitting algorithm with the distance constraint. Compatibility was used to determine the need for merging growing results to obtain roof feature lines. Lastly, according to the distribution characteristics of the original points near the feature lines, the endpoints of the feature lines were determined and optimized. Experiments were conducted using two representative building datasets. The results of the experiments showed that the proposed algorithm could directly extract high-quality roof feature lines from point clouds for both single buildings and multiple buildings.<\/jats:p>","DOI":"10.3390\/rs16162969","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T13:35:49Z","timestamp":1723556149000},"page":"2969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Feature Line Extraction Method for Building Roof Point Clouds Considering the Grid Center of Gravity Distribution"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7514-7951","authenticated-orcid":false,"given":"Jinzheng","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8816-4362","authenticated-orcid":false,"given":"Jingxue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"},{"name":"Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9709-7168","authenticated-orcid":false,"given":"Dongdong","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2598-0047","authenticated-orcid":false,"given":"Xiao","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment Computation & Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2020.11.011","article-title":"Automatic 3D building reconstruction from multi-view aerial images with deep learning","volume":"171","author":"Yu","year":"2021","journal-title":"ISPRS J. 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