{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:20:44Z","timestamp":1772720444782,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["61871320"],"award-info":[{"award-number":["61871320"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872291"],"award-info":[{"award-number":["61872291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["17JS099"],"award-info":[{"award-number":["17JS099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi key Laboratory project","award":["61871320"],"award-info":[{"award-number":["61871320"]}]},{"name":"Shaanxi key Laboratory project","award":["61872291"],"award-info":[{"award-number":["61872291"]}]},{"name":"Shaanxi key Laboratory project","award":["17JS099"],"award-info":[{"award-number":["17JS099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.<\/jats:p>","DOI":"10.3390\/rs14194926","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9764-5400","authenticated-orcid":false,"given":"Xiaojuan","family":"Ning","sequence":"first","affiliation":[{"name":"Institute of Computer Science and Engineering, Xi\u2019an University of Technology, No.5 South of Jinhua Road, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory of Network Computing and Security Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishu","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Engineering, Xi\u2019an University of Technology, No.5 South of Jinhua Road, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Hou","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Engineering, Xi\u2019an University of Technology, No.5 South of Jinhua Road, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Engineering, Xi\u2019an University of Technology, No.5 South of Jinhua Road, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory of Network Computing and Security Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3742-4029","authenticated-orcid":false,"given":"Haiyan","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Engineering, Xi\u2019an University of Technology, No.5 South of Jinhua Road, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory of Network Computing and Security Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 of Lihu Road, Wuxi 214122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110440","DOI":"10.1016\/j.measurement.2021.110440","article-title":"A branch-trunk-constrained hierarchical clustering method for street trees individual extraction from mobile laser scanning point clouds","volume":"189","author":"Li","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.isprsjprs.2017.03.012","article-title":"SigVox\u2014A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds","volume":"128","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1080\/01431161.2019.1662966","article-title":"Identification of trees and their trunks from mobile laser scanning data of roadway scenes","volume":"41","author":"Yadav","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Du, S., Lindenbergh, R., Ledoux, H., Stoter, J., and Nan, L. (2019). AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees. Remote Sens., 11.","DOI":"10.20944\/preprints201907.0058.v2"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"584","DOI":"10.3390\/rs5020584","article-title":"A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data","volume":"5","author":"Wu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_6","first-page":"91","article-title":"Biomass estimation of individual trees using stem and crown diameter TLS measurements","volume":"3812","author":"Holopainen","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sparks, A.M., Corrao, M.V., and Smith, A.M.S. (2022). Cross-Comparison of Individual Tree Detection Methods Using Low and High Pulse Density Airborne Laser Scanning Data. Remote Sens., 14.","DOI":"10.3390\/rs14143480"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ku\u017eelka, K., Slav\u00edk, M., and Surov\u00fd, P. (2020). Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement. Remote Sens., 12.","DOI":"10.3390\/rs12081236"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Windrim, L., and Bryson, M. (2020). Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12091469"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wan, P., Wang, T., Cai, S., Chen, Y., Jin, X., and Yan, G. (2019). A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data. Remote Sens., 11.","DOI":"10.3390\/rs11020211"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Brolly, G., Kir\u00e1ly, G., Lehtom\u00e4ki, M., and Liang, X. (2021). Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory. Remote Sens., 13.","DOI":"10.3390\/rs13040542"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kolendo, \u0141., Kozniewski, M., Ksepko, M., Chmur, S., and Neroj, B. (2021). Parameterization of the Individual Tree Detection Method Using Large Dataset from Ground Sample Plots and Airborne Laser Scanning for Stands Inventory in Coniferous Forest. Remote Sens., 13.","DOI":"10.3390\/rs13142753"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gollob, C., Ritter, T., Wassermann, C., and Nothdurft, A. (2019). Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots. Remote Sens., 11.","DOI":"10.3390\/rs11131602"},{"key":"ref_14","first-page":"164","article-title":"Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning","volume":"69","author":"Cabo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Oveland, I., Hauglin, M., Giannetti, F., Kj\u00f8rsvik, N.S., and Gobakken, T. (2018). Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection. Remote Sens., 10.","DOI":"10.3390\/rs10040538"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TGRS.2020.2996064","article-title":"Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery","volume":"59","author":"Lv","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, Z., Hoegner, L., Stilla, U., and Yao, W. (2018, January 19\u201320). Instance Segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and Graph-Based Optimization. Proceedings of the 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China.","DOI":"10.1109\/PRRS.2018.8486220"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/JSTARS.2016.2565519","article-title":"Segmentation of Individual Trees From TLS and MLS Data","volume":"10","author":"Zhong","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yan, W., Guan, H., Cao, L., Yu, Y., Gao, S., and Lu, J. (2018). An Automated Hierarchical Approach for Three-Dimensional Segmentation of Single Trees Using UAV LiDAR Data. Remote. Sens., 10.","DOI":"10.3390\/rs10121999"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2016.07.009","article-title":"A dual growing method for the automatic extraction of individual trees from mobile laser scanning data","volume":"120","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","first-page":"100371","article-title":"An automated approach for street trees detection using mobile laser scanner data","volume":"20","author":"Husain","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112098","DOI":"10.1016\/j.rse.2020.112098","article-title":"A method for vegetation extraction in mountainous terrain for rockfall simulation","volume":"251","author":"Bonneau","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.isprsjprs.2021.03.002","article-title":"Individual tree extraction from urban mobile laser scanning point clouds using deep pointwise direction embedding","volume":"175","author":"Luo","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/JSTARS.2020.2979369","article-title":"An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds","volume":"13","author":"Yang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2015.10.007","article-title":"Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories","volume":"110","author":"Tao","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/2150704X.2018.1444286","article-title":"A supervoxel approach to the segmentation of individual trees from LiDAR point clouds","volume":"9","author":"Xu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1109\/JSTARS.2019.2929546","article-title":"Rapid Urban Roadside Tree Inventory Using a Mobile Laser Scanning System","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"33357","DOI":"10.1007\/s11042-021-11328-7","article-title":"Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds","volume":"80","author":"Ning","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cag.2015.01.006","article-title":"Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas","volume":"49","author":"Weinmann","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Munoz, D., Bagnell, J.A., Vandapel, N., and Hebert, M. (2009, January 20\u201325). Contextual classification with functional Max-Margin Markov Networks. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206590"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Trochta, J., Kr\u016f\u010dek, M., Vr\u0161ka, T., and Kr\u00e1l, K. (2017). 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176871"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4926\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:23Z","timestamp":1760143523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194926"],"URL":"https:\/\/doi.org\/10.3390\/rs14194926","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}