{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:01:27Z","timestamp":1771466487668,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683263","type":"print"},{"value":"9781643683270","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:p>Airborne topographic LiDAR is an active remote sensing technology that emits near-infrared light to map objects on the Earth\u2019s surface. Derived products of LiDAR are suitable to service a wide range of applications because of their rich three-dimensional spatial information and their capacity to obtain multiple returns. However, processing point cloud data still requires a large effort in manual editing. Certain human-made objects are difficult to detect because of their variety of shapes, irregularly-distributed point clouds, and a low number of class samples. In this work, we propose an end-to-end deep learning framework to automatize the detection and segmentation of objects defined by an arbitrary number of LiDAR points surrounded by clutter. Our method is based on a light version of PointNet that achieves good performance on both object recognition and segmentation tasks. The results are tested against manually delineated power transmission towers and show promising accuracy.<\/jats:p>","DOI":"10.3233\/faia220347","type":"book-chapter","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:31:38Z","timestamp":1666009898000},"source":"Crossref","is-referenced-by-count":3,"title":["Object Segmentation of Cluttered Airborne LiDAR Point Clouds"],"prefix":"10.3233","author":[{"given":"Mariona","family":"Car\u00f3s","sequence":"first","affiliation":[{"name":"Departament de Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona (UB), Gran Via Corts Catalanes, 585, 08007 Barcelona, Spain"}]},{"given":"Ariadna","family":"Just","sequence":"additional","affiliation":[{"name":"Institut Cartogr\u00e0fic i Geol\u00f2gic de Catalunya, Barcelona, Spain"}]},{"given":"Santi","family":"Segu\u00ed","sequence":"additional","affiliation":[{"name":"Departament de Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona (UB), Gran Via Corts Catalanes, 585, 08007 Barcelona, Spain"}]},{"given":"Jordi","family":"Vitri\u00e0","sequence":"additional","affiliation":[{"name":"Departament de Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona (UB), Gran Via Corts Catalanes, 585, 08007 Barcelona, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:31:43Z","timestamp":1666009903000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"ISBN":["9781643683263","9781643683270"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220347","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]}}}