{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T01:24:26Z","timestamp":1782437066089,"version":"3.54.5"},"reference-count":48,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["61771456"],"award-info":[{"award-number":["61771456"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["42201487"],"award-info":[{"award-number":["42201487"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["E1330603"],"award-info":[{"award-number":["E1330603"]}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["61771456"],"award-info":[{"award-number":["61771456"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["42201487"],"award-info":[{"award-number":["42201487"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["E1330603"],"award-info":[{"award-number":["E1330603"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.<\/jats:p>","DOI":"10.3390\/rs14205272","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0130-1457","authenticated-orcid":false,"given":"Lele","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhu","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueqian","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, No.8 Fochengxi Road, Nanjing 211100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liang","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, No 4, South Fourth Street, Zhong Guan Cun, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","first-page":"2816","article-title":"Center Line Coordinates Survey for Existing Railway by 3D Constraints Method","volume":"11","author":"Wei","year":"2013","journal-title":"TELKOMNIKA Indones. J. Electr. Eng."},{"key":"ref_2","first-page":"115","article-title":"The Airborne Laser Radar Technology and Its Application in Power Engineering","volume":"30","author":"Luo","year":"2015","journal-title":"Shanxi Sci. Technol."},{"key":"ref_3","unstructured":"Vosselman, G., and Hans-Gerd, M. (2011). Airborne and Terrestrial Laser Scanning, Whittles Publishing."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"052025","DOI":"10.1088\/1757-899X\/382\/5\/052025","article-title":"Application of LiDAR technology in power line inspection","volume":"382","author":"Li","year":"2018","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_5","unstructured":"Hosford, S., Baghdadi, N., Bourgine, B., Daniels, P., and King, C. (2003, January 21\u201325). Fusion of airborne laser altimeter and RADARSAT data for DEM generation. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chiu, C.L., Fei, L.Y., Liu, J.K., and Wu, M.C. (2015, January 26\u201331). National airborne LiDAR mapping and examples for applications in deep-seated landslides in Taiwan. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326875"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2004.05.004","article-title":"Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds","volume":"59","author":"Sithole","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rasib, A.W., Ismail, Z., Rahman, M.Z., Jamaluddin, S., Kadir, W.H., Ariffin, A., Razak, K.A., and Kang, C.S. (2013, January 21\u201326). Extraction of Digital Terrain Model (DTM) over vegetated area in tropical rainforest using LiDAR. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723556"},{"key":"ref_9","unstructured":"Wang, Q., Ni-Meister, W., Ni, W., and Pang, Y. (August, January 28). The Potential of Forest Biomass Inversion Based on Canopy-Independent Structure Metrics Tested by Airborne LiDAR Data. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2013.02.004","article-title":"A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data","volume":"79","author":"Huang","year":"2013","journal-title":"Isprs J. Photogramm. Remote. Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3832\/ifor1780-009","article-title":"Integration between TLS and UAV photogrammetry techniques for forestry applications","volume":"10","author":"Aicardi","year":"2017","journal-title":"IForest"},{"key":"ref_12","first-page":"1","article-title":"Dynamic graph Cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17077","DOI":"10.3390\/rs71215870","article-title":"3D maize plant reconstruction based on georeferenced overlapping lidar point clouds","volume":"7","author":"Garrido","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4489","DOI":"10.1109\/JSTARS.2015.2496358","article-title":"Combined Use of Airborne LiDAR and Satellite GF-1 Data to Estimate Leaf Area Index, Height, and Aboveground Biomass of Maize during Peak Growing Season","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","first-page":"451","article-title":"The way forward: Advances in maintaining right-of-way of transmission lines","volume":"64","author":"Ituen","year":"2010","journal-title":"Geomatica"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"547","DOI":"10.5194\/isprs-archives-XLII-2-W13-547-2019","article-title":"Real-time powerline corridor inspection by edge computing of uav lidar data","volume":"XLII-2\/W13","author":"Pu","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/JSTARS.2019.2893967","article-title":"Power Line Extraction From Mobile LiDAR Point Clouds","volume":"12","author":"Xu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8637","DOI":"10.1109\/TGRS.2020.2989470","article-title":"Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping","volume":"58","author":"Nardinocchi","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ceron, A., Mondragon B., I.F., and Prieto, F. (2014, January 27\u201330). Power line detection using a circle based search with UAV images. Proceedings of the 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014\u2014Conference Proceedings, Orlando, FL, USA.","DOI":"10.1109\/ICUAS.2014.6842307"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Munir, N., Awrangjeb, M., and Stantic, B. (December, January 29). An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using LiDAR data. Proceedings of the 2020 Digital Image Computing: Techniques and Applications, DICTA 2020, Melbourne, Australia.","DOI":"10.1109\/DICTA51227.2020.9363391"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, Y., Du, Y., and Shi, W. (2021). Fast and accurate power line corridor survey using spatial line clustering of point cloud. Remote Sens., 13.","DOI":"10.3390\/rs13081571"},{"key":"ref_23","first-page":"1","article-title":"A GCN-Based Method for Extracting Power Lines and Pylons from Airborne LiDAR Data","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2016, January 21\u201326). PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA."},{"key":"ref_25","unstructured":"Melzer, T., and Briese, C. (2004, January 22\u201323). Extraction and Modeling of Power Lines from ALS Point Clouds. Proceedings of the 28th Workshop of the Austrian Association for Pattern Recognition, Hagenberg, Austria."},{"key":"ref_26","unstructured":"Jing, L., Zhang, J., Deng, K., Liu, Z., and Shi, Q. (2011, January 9\u201311). A New Power-Line Extraction Method Based on Airborne LiDAR Point Cloud Data. Proceedings of the 2011 International Symposium on Image and Data Fusion, Tengchong, China."},{"key":"ref_27","unstructured":"Sohn, G., Jwa, Y., and Kim, H.B. (September, January 25). Automatic powerline scene classification and reconstruction using airborne LiDAR data. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia."},{"key":"ref_28","first-page":"258","article-title":"Extraction of power lines using mobile LiDAR data of roadway environment","volume":"8","author":"Yadav","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102802","DOI":"10.1016\/j.autcon.2019.03.023","article-title":"Power line mapping technique using all-terrain mobile laser scanning","volume":"105","author":"Kukko","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_30","unstructured":"Le Cam, L.M., and Neyman, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability\u2014Vol. 1, University of California Press."},{"key":"ref_31","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_32","unstructured":"(2019). Agglomerative Hierarchical Clustering. Clustering Methodology for Symbolic Data, John Wiley & Sons, Ltd.. Chapter 8."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3302","DOI":"10.3390\/rs6043302","article-title":"Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data","volume":"6","author":"Cheng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/01431161.2015.1125549","article-title":"Extraction of power-transmission lines from vehicle-borne lidar data","volume":"37","author":"Guan","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","first-page":"17","article-title":"On extracting power-line from airborne LiDAR point cloud data","volume":"7","author":"Liang","year":"2012","journal-title":"Bull. Surv. Mapp."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","unstructured":"Peng, S., Xi, X., Wang, C., Dong, P., Wang, P., and Nie, S. (2019). Systematic comparison of power corridor classification methods from ALS point clouds. Remote Sens., 11.","DOI":"10.3390\/rs11171961"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1109\/TGRS.2019.2926397","article-title":"Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, J., Lei, B., Song, Q., Ying, H., Chen, D.Z., and Wu, J. (2020, January 14\u201319). A Hierarchical Graph Network for 3D Object Detection on Point Clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00047"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1109\/TGRS.2017.2777868","article-title":"SAR image classification via deep recurrent encoding neural networks","volume":"56","author":"Geng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xu, M., Ding, R., Zhao, H., and Qi, X. (2021, January 20\u201325). PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6699","DOI":"10.1109\/TGRS.2018.2841808","article-title":"Vehicle Instance Segmentation from Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 14\u201319). Randla-Net: Efficient semantic segmentation of large-scale point clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.isprsjprs.2019.10.011","article-title":"Deep learning for conifer\/deciduous classification of airborne LiDAR 3D point clouds representing individual trees","volume":"158","author":"Hamraz","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1002\/rob.20134","article-title":"Natural terrain classification using three-dimensional ladar data for ground robot mobility","volume":"23","author":"Lalonde","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_48","unstructured":"(2022, August 25). AHN3 Download. Available online: https:\/\/app.pdok.nl\/ahn3-downloadpage\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:59:10Z","timestamp":1760144350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":48,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205272"],"URL":"https:\/\/doi.org\/10.3390\/rs14205272","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}