{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:01:21Z","timestamp":1771466481090,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006761","name":"Universidade de Vigo","doi-asserted-by":"publisher","award":["00VI 131H 6410211"],"award-info":[{"award-number":["00VI 131H 6410211"]}],"id":[{"id":"10.13039\/501100006761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["769255"],"award-info":[{"award-number":["769255"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway\u2019s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points\u2019 classification, together with the results of the measures performed.<\/jats:p>","DOI":"10.3390\/rs11212567","type":"journal-article","created":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T12:30:50Z","timestamp":1572611450000},"page":"2567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Automated Inspection of Railway Tunnels\u2019 Power Line Using LiDAR Point Clouds"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-2104","authenticated-orcid":false,"given":"Ana","family":"S\u00e1nchez-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-2225","authenticated-orcid":false,"given":"Mario","family":"Soil\u00e1n","sequence":"additional","affiliation":[{"name":"Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain"}]},{"given":"Manuel","family":"Cabaleiro","sequence":"additional","affiliation":[{"name":"Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3547-8907","authenticated-orcid":false,"given":"Pedro","family":"Arias","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Engineering, School of Mining Engineering, University of Vigo, 36310 Vigo, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,1]]},"reference":[{"key":"ref_1","unstructured":"(2019, August 12). FaroArm\u00ae|FARO SPAIN, S.L.U.. Available online: https:\/\/www.faro.com\/es-es\/productos\/3d-manufacturing\/faroarm\/."},{"key":"ref_2","unstructured":"Adif (2019, February 06). Ministerio de Fomento, Gobierno de Espa\u00f1a. Available online: http:\/\/www.adif.es\/."},{"key":"ref_3","unstructured":"Gil Calvo, M.A., Jim\u00e9nez Cano, A., and Est\u00e9vez C\u00e1rdenas, F. (2008). L\u00ednea A\u00e9rea de Contacto unificado para Catenarias CA-160 y CA-220, ADIF. [2nd ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arastounia, M. (2017). An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds. Infrastructures, 2.","DOI":"10.3390\/infrastructures2020008"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"553","DOI":"10.5194\/isprsarchives-XL-5-553-2014","article-title":"Extracting Rail Track Geometry from Static Terrestrial Laser Scans for Monitoring Purposes","volume":"XL\u20135","author":"Soni","year":"2014","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Collin, B., Carreaud, P., and Lan\u00e7on, H. (2016, January 10\u201315). High Efficiency Techniques for the Assessment of Railways Infrastructures and Buildings. Proceedings of the Transportation Research Procedia, Shanghai, China.","DOI":"10.1016\/j.trpro.2016.05.153"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Soil\u00e1n, M., S\u00e1nchez-Rodr\u00edguez, A., del R\u00edo-Barral, P., Perez-Collazo, C., Arias, P., and Riveiro, B. (2019). Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring. Infrastructures, 4.","DOI":"10.3390\/infrastructures4040058"},{"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","unstructured":"Leslar, M., Perry, G., and McNease, K. (2010, January 26\u201330). Using mobile lidar to survey a railway line for asset inventory. Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference 2010: Opportunities for Emerging Geospatial Technologies, San Diego, CA, USA."},{"key":"ref_10","unstructured":"Arastounia, M. (2012). Automatic Classification of LiDAR Point Clouds in A Railway Environment, University of Twente Faculty of Geo-Information and Earth Observation."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14916","DOI":"10.3390\/rs71114916","article-title":"Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data","volume":"7","author":"Arastounia","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Arastounia, M., and Elberink, S.O. (2016). Application of Template Matching for Improving Classification of Urban Railroad Point Clouds. Sensors, 16.","DOI":"10.3390\/s16122112"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, C., Yang, Z., Chen, Y., and Li, J. (2016, January 12\u201319). Automatic railway power line extraction using mobile laser scanning data. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives, Prague, Czech.","DOI":"10.5194\/isprsarchives-XLI-B5-615-2016"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, B., Li, Q., Huang, X., and Wang, C. (2016). An Improved Method for Power-Line Reconstruction from Point Cloud Data. Remote Sens., 8.","DOI":"10.3390\/rs8010036"},{"key":"ref_16","unstructured":"Wang, Y., Chen, Q., Liu, L., and Li, K. (2018). A Hierarchical unsupervised method for power line classification from airborne LiDAR data. Int. J. Digit. Earth, 1\u201317."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Blug, A., Baulig, C., Wolfelschneider, H., and Hofler, H. (2004, January 14\u201317). Fast fiber coupled clearance profile scanner using real time 3D data processing with automatic rail detection. Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy.","DOI":"10.1109\/IVS.2004.1336462"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mikrut, S., Kohut, P., Pyka, K., Tokarczyk, R., Barszcz, T., and Uhl, T. (2016). Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook. Sensors, 16.","DOI":"10.3390\/s16050683"},{"key":"ref_19","unstructured":"Luo, C., Jwa, Y., and Sohn, G. (2014). Context based multiple railway object recognition from mobile laser scanning data. Int. Geosci. Remote Sens. Symp., 3602\u20133605."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.autcon.2018.09.014","article-title":"Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets","volume":"96","author":"Riveiro","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_21","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2016). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conf. Comput. Vis. Pattern Recognit., 601\u2013610."},{"key":"ref_22","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tatarchenko, M., Dosovitskiy, A., and Brox, T. (2017, January 22\u201329). Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.230"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., and Kautz, J. (2018, January 18\u201322). SPLATNet: Sparse Lattice Networks for Point Cloud Processing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00268"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.procs.2018.07.222","article-title":"Automatic CORINE land cover classification from airborne LIDAR data","volume":"126","author":"Balado","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Balado, J., Mart\u00ednez-s\u00e1nchez, J., Arias, P., and Novo, A. (2019). Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data. Sensors, 19.","DOI":"10.3390\/s19163466"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2019.01.024","article-title":"Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments","volume":"150","author":"Luo","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.isprsjprs.2018.11.006","article-title":"A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training","volume":"147","author":"Kumar","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Hem\u00e1ndez Puertas, J. (2016). C\u00e1lculo de Esfuerzos y Desplazamientos sobre P\u00f3rticos CA-220 con Matlab, Universidad Pontificia Comillas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.isprsjprs.2016.01.019","article-title":"Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory","volume":"114","author":"Riveiro","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","unstructured":"(1970, January 01). MathWorks-Makers of MATLAB and Simulink. Available online: https:\/\/es.mathworks.com\/."},{"key":"ref_32","unstructured":"(2019, June 11). Teledyne Optech \u00a9 Teledyne Optech. Available online: http:\/\/www.teledyneoptech.com\/en\/home\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.optlastec.2012.05.029","article-title":"Accuracy verification of the Lynx Mobile Mapper system","volume":"45","author":"Puente","year":"2013","journal-title":"Opt. Laser Technol."},{"key":"ref_34","unstructured":"D\u00edaz, O. (2019, January 30). Understanding Accuracy in Laser Scanners|SmartGeoMetrics. Available online: http:\/\/www.smartgeometrics.com\/2013\/08\/28\/understanding-accuracy-in-laser-scanners-2\/."},{"key":"ref_35","unstructured":"(2019, June 25). RIEGL\u2014RIEGL Laser Measurement Systems. Available online: http:\/\/www.riegl.com\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.measurement.2016.10.009","article-title":"Quantifying the influence of rain in LiDAR performance","volume":"95","author":"Filgueira","year":"2017","journal-title":"Meas. J. Int. Meas. 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