{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T01:39:43Z","timestamp":1774661983308,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Poland","award":["23.610.007-110"],"award-info":[{"award-number":["23.610.007-110"]}]},{"name":"Visimind Ltd.","award":["23.610.007-110"],"award-info":[{"award-number":["23.610.007-110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The classification of airborne LiDAR data is a prerequisite for many spatial data elaborations and analysis. In the domain of power supply networks, it is of utmost importance to be able to discern at least five classes for further processing\u2014ground, buildings, vegetation, poles, and catenaries. This process is mainly performed manually by domain experts with the use of advanced point cloud manipulation software. The goal of this paper is to find a set of features which would divide space well enough to achieve accurate automatic classification on all relevant classes within the domain, thus reducing manual labor. To tackle this problem, we propose a single multi-class approach to classify all four basic classes (excluding ground) in a power supply domain with single pass-through, using one network. The proposed solution implements random forests and gradient boosting to create a feature-based per-point classifier which achieved an accuracy and F1 score of over 99% on all tested cases, with the maximum of 99.7% for accuracy and 99.5% for F1 score. Moreover, we achieved a maximum of 81.7% F1 score for the most sparse class. The results show that the proposed set of features for the LiDAR data cloud is effective in power supply line classification.<\/jats:p>","DOI":"10.3390\/rs15030561","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T02:31:11Z","timestamp":1674009071000},"page":"561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Feature Selection for Airbone LiDAR Point Cloud Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Mateusz","family":"Kuprowski","sequence":"first","affiliation":[{"name":"Visimind Ltd., 10-683 Olsztyn, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3163-9408","authenticated-orcid":false,"given":"Pawel","family":"Drozda","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, 10-718 Olsztyn, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cai, Z., Ma, H., and Zhang, L. (2019). A Building Detection Method Based on Semi-Suppressed Fuzzy C-Means and Restricted Region Growing Using Airborne LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11070848"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, K., Ma, H., Ma, H., Cai, Z., and Zhang, L. (2020). Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing. Remote Sens., 12.","DOI":"10.3390\/rs12172849"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"29","DOI":"10.36548\/jscp.2021.1.004","article-title":"Building Detection using Two-Layered Novel Convolutional Neural Networks","volume":"3","author":"Karuppusamy","year":"2021","journal-title":"J. Soft Comput. Paradig."},{"key":"ref_4","first-page":"1","article-title":"Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data","volume":"37","author":"Vayghan","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.aei.2018.04.002","article-title":"Automated residential building detection from airborne LiDAR data with deep neural networks","volume":"36","author":"Zhou","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101033","DOI":"10.1016\/j.aei.2020.101033","article-title":"Data mining for recognition of spatial distribution patterns of building heights using airborne lidar data","volume":"43","author":"Shirowzhan","year":"2020","journal-title":"Adv. Eng. Inf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.isprsjprs.2013.10.004","article-title":"Results of the ISPRS benchmark on urban object detection and 3D building reconstruction","volume":"93","author":"Rottensteiner","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Che, E., Jung, J., and Olsen, M.J. (2019). Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors, 19.","DOI":"10.3390\/s19040810"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1109\/TGRS.2020.2969024","article-title":"Classification of Hyperspectral and LiDAR Data Using Coupled CNNs","volume":"58","author":"Hang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","first-page":"1","article-title":"Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data","volume":"19","author":"Hong","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","first-page":"207","article-title":"Airborne LiDAR feature selection for urban classification using random forests","volume":"38","author":"Chehata","year":"2009","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2010.08.007","article-title":"Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests","volume":"66","author":"Guo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.isprsjprs.2014.04.015","article-title":"Classification of airborne laser scanning data using JointBoost","volume":"100","author":"Guo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5194\/isprs-annals-IV-2-W4-165-2017","article-title":"Airborne lidar power line classification based on spatial topological structure characteristics","volume":"IV-2\/W4","author":"Wang","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"167","DOI":"10.5194\/isprsannals-I-3-167-2012","article-title":"Automatic powerline scene classification and reconstruction using airborne lidar data","volume":"1\u20133","author":"Sohn","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Awrangjeb, M. (2019). Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels. Remote Sens., 11.","DOI":"10.3390\/rs11151798"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chasco-Hern\u00e1ndez, D., Sanz-Delgado, J.A., Garc\u00eda-Morales, V., and \u00c1lvarez-Mozos, J. (2020). Automatic Detection of High-Voltage Power Lines in LiDAR Surveys Using Data Mining Techniques. Advances in Design Engineering. Lecture Notes in Mechanical Engineering, Springer.","DOI":"10.1007\/978-3-030-41200-5_62"},{"key":"ref_18","first-page":"1","article-title":"A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5194\/isprs-annals-IV-4-W8-91-2019","article-title":"Voxel-Based Extraction of Individual Pylons and Wires from LiDAR Point Cloud Data","volume":"IV-4\/W8","author":"Munir","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Munir, N., Awrangjeb, M., and Stantic, B. (2020). Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12183078"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2019.03.021","article-title":"Characterization and modeling of power line corridor elements from LiDAR point clouds","volume":"152","author":"Ortega","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tu, X., Xu, C., Liu, S., Lin, S., Chen, L., Xie, G., and Li, R. (2020). LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection. Sensors, 20.","DOI":"10.3390\/s20216387"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Zheng, D., Li, C., and Li, K. (2017). Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas. Remote Sens., 9.","DOI":"10.3390\/rs9080771"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, R., Jiang, W., and Jiang, S. (2018). A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10122051"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5514205","DOI":"10.1109\/LGRS.2022.3225215","article-title":"SSTNet: Spatial, Spectral, and Texture Aware Attention Network Using Hyperspectral Image for Corn Variety Identification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TCYB.2020.2969255","article-title":"ASIF-Net: Attention Steered Interweave Fusion Network for RGB-D Salient Object Detection","volume":"51","author":"Li","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, S., Xu, C., Chen, L., Li, S., and Tu, X. (2020). LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning. Sensors, 20.","DOI":"10.3390\/s20082212"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pastucha, E. (2016). Catenary System Detection, Localization and Classification Using Mobile Scanning Data. Remote Sens., 8.","DOI":"10.3390\/rs8100801"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wienmann, M., Schmidt, A., Mallet, C., Hinz, S., Rottensteiner, F., and Jutzi, B. (2015, January 25\u201327). Contextual classification of point cloud data by exploiting individual 3D neighborhoods. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Munich, Germany.","DOI":"10.5194\/isprsannals-II-3-W4-271-2015"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/561\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:08:35Z","timestamp":1760119715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030561"],"URL":"https:\/\/doi.org\/10.3390\/rs15030561","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]}}}