{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:33:13Z","timestamp":1774153993419,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:00:00Z","timestamp":1681084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41771439"],"award-info":[{"award-number":["41771439"]}]},{"name":"National Natural Science Foundation of China","award":["KYCX18_1206"],"award-info":[{"award-number":["KYCX18_1206"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["41771439"],"award-info":[{"award-number":["41771439"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX18_1206"],"award-info":[{"award-number":["KYCX18_1206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology research. However, previous works show under- or over-segmentation effects for road-side trees due to overlapping, irregular shapes and incompleteness. In this paper, a deep learning framework that combines semantic and instance segmentation is proposed to extract single road-side trees from vehicle-mounted mobile laser scanning (MLS) point clouds. In the semantic segmentation stage, the ground points are filtered to reduce the processing time. Subsequently, a graph-based semantic segmentation network is developed to segment road-side tree points from the raw MLS point clouds. For the individual tree segmentation stage, a novel joint instance and semantic segmentation network is adopted to detect instance-level roadside trees. Two complex Chinese urban point cloud scenes are used to evaluate the individual urban tree segmentation performance of the proposed method. The proposed method accurately extract approximately 90% of the road-side trees and achieve better segmentation results than existing published methods in both two urban MLS point clouds. Living Vegetation Volume (LVV) calculation can benefit from individual tree segmentation. The proposed method provides a promising solution for ecological construction based on the LVV calculation of urban roads.<\/jats:p>","DOI":"10.3390\/rs15081992","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Pengcheng","family":"Wang","sequence":"first","affiliation":[{"name":"Blueprint Idea Technology Development Company Limited, Beijing 100020, China"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[{"name":"Beijing Career International Company Limited, Beijing 100020, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210093, China"}]},{"given":"Zefan","family":"Liao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"given":"Yao","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}]},{"given":"Lei","family":"Dai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Shan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210093, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0104-1969","authenticated-orcid":false,"given":"Tengping","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210093, China"},{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112857","DOI":"10.1016\/j.rse.2021.112857","article-title":"Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)","volume":"270","author":"Huo","year":"2022","journal-title":"Remote Sens. 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