{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:28:45Z","timestamp":1776356925406,"version":"3.51.2"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2015,9,28]],"date-time":"2015-09-28T00:00:00Z","timestamp":1443398400000},"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>Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.<\/jats:p>","DOI":"10.3390\/rs71012680","type":"journal-article","created":{"date-parts":[[2015,9,28]],"date-time":"2015-09-28T10:13:46Z","timestamp":1443435226000},"page":"12680-12703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Automatic Detection and Classification of Pole-Like  Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm"],"prefix":"10.3390","volume":"7","author":[{"given":"Borja","family":"Rodr\u00edguez-Cuenca","sequence":"first","affiliation":[{"name":"Department of Physics and Mathematics, University of Alcal\u00e1, Campus Universitario Ctra.,  Alcal\u00e1 de Henares, 28871 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5695-1268","authenticated-orcid":false,"given":"Silverio","family":"Garc\u00eda-Cort\u00e9s","sequence":"additional","affiliation":[{"name":"Department of Mining Exploitation, University of Oviedo, Escuela Polit\u00e9cnica de Mieres,  Gonzalo Guti\u00e9rrez Quir\u00f3s, 33600 Mieres, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6912-6299","authenticated-orcid":false,"given":"Celestino","family":"Ord\u00f3\u00f1ez","sequence":"additional","affiliation":[{"name":"Department of Mining Exploitation, University of Oviedo, Escuela Polit\u00e9cnica de Mieres,  Gonzalo Guti\u00e9rrez Quir\u00f3s, 33600 Mieres, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Alonso","sequence":"additional","affiliation":[{"name":"Department of Physics and Mathematics, University of Alcal\u00e1, Campus Universitario Ctra.,  Alcal\u00e1 de Henares, 28871 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2831","DOI":"10.1080\/01431160500117865","article-title":"Analysis of land use\/cover changes and urban expansion of Nairobi city using remote sensing and GIS","volume":"26","author":"Mundia","year":"2005","journal-title":"Int. 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