{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:22:55Z","timestamp":1768821775239,"version":"3.49.0"},"reference-count":87,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,16]],"date-time":"2017-03-16T00:00:00Z","timestamp":1489622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["FP7-ICT-2011-318787"],"award-info":[{"award-number":["FP7-ICT-2011-318787"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as \u201ctree points\u201d and \u201cother points\u201d. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the \u201ctree points\u201d. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains     10.13     M labeled 3D points among which     17.6    % are labeled as \u201ctree points\u201d. The derived results clearly reveal a semantic classification of high accuracy (up to     90.77    %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than     2.5     h).<\/jats:p>","DOI":"10.3390\/rs9030277","type":"journal-article","created":{"date-parts":[[2017,3,16]],"date-time":"2017-03-16T11:24:28Z","timestamp":1489663468000},"page":"277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas"],"prefix":"10.3390","volume":"9","author":[{"given":"Martin","family":"Weinmann","sequence":"first","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, D-76131 Karlsruhe, Germany"},{"name":"Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mand\u00e9, France"}]},{"given":"Michael","family":"Weinmann","sequence":"additional","affiliation":[{"name":"Institute of Computer Science II, University of Bonn, Friedrich-Ebert-Allee 144, D-53113 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2675-165X","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"Mallet","sequence":"additional","affiliation":[{"name":"Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mand\u00e9, France"}]},{"given":"Mathieu","family":"Br\u00e9dif","sequence":"additional","affiliation":[{"name":"Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mand\u00e9, France"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Munoz, D., Bagnell, J.A., Vandapel, N., and Hebert, M. (2009, January 20\u201325). 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