{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:29:16Z","timestamp":1763706556979,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem\u2014how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models\u2019 high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel\u2019s location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%.<\/jats:p>","DOI":"10.3390\/rs15092317","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T01:33:40Z","timestamp":1682645620000},"page":"2317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas"],"prefix":"10.3390","volume":"15","author":[{"given":"Mahdiye","family":"Zaboli","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 141663, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2767-3462","authenticated-orcid":false,"given":"Heidar","family":"Rastiveis","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 141663, Iran"},{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8212-2639","authenticated-orcid":false,"given":"Benyamin","family":"Hosseiny","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 141663, Iran"}]},{"given":"Danesh","family":"Shokri","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 141663, Iran"}]},{"given":"Wayne A.","family":"Sarasua","sequence":"additional","affiliation":[{"name":"Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-5356","authenticated-orcid":false,"given":"Saeid","family":"Homayouni","sequence":"additional","affiliation":[{"name":"Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Quebec City, QC G1K 9A9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55649","DOI":"10.1109\/ACCESS.2019.2909742","article-title":"3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TGRS.2015.2476502","article-title":"Object Classification and Recognition from Mobile Laser Scanning Point Clouds in a Road Environment","volume":"54","author":"Jaakkola","year":"2016","journal-title":"IEEE Trans. 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