{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:52:46Z","timestamp":1780595566131,"version":"3.54.1"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2015,5,4]],"date-time":"2015-05-04T00:00:00Z","timestamp":1430697600000},"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>In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser Scanning systems are able to capture the 3D environment of the rail tracks with a high level of detail. Our approach first detects laser points that were reflected by the rail tracks, by making use of local properties such as parallelism and height in relation to neighboring objects. In the modeling stage, we present two approaches to determine the centerline location. The first approach generates center points in a data-driven manner by projecting rail track points to the parallel track, and taking the midpoint as initial center point. Next, a piecewise linear function is fitted through the center points to generate center points at a regular interval. The second approach models the rail track by fitting piecewise 3D track models to the rail track points. The model consists of a pair of two parallel rail tracks. The fitted pieces are smoothened by a Fourier series interpolation function. After that the centerline is implicitly determined by the geometric center of the pair of tracks. Reference data has been used to analyze the quality of our results, confirming that the position of the centerlines can be determined with an accuracy of 2\u20133 cm.<\/jats:p>","DOI":"10.3390\/rs70505565","type":"journal-article","created":{"date-parts":[[2015,5,4]],"date-time":"2015-05-04T10:33:04Z","timestamp":1430735584000},"page":"5565-5583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Automatic Extraction of Railroad Centerlines from Mobile Laser Scanning Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4511-2095","authenticated-orcid":false,"given":"Sander","family":"Elberink","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6639-1727","authenticated-orcid":false,"given":"Kourosh","family":"Khoshelham","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands"},{"name":"Department of Infrastructure Engineering, University of Melbourne, Melbourne 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,5,4]]},"reference":[{"key":"ref_1","first-page":"32","article-title":"Mobile mapping systems: An introduction to the technology","volume":"13","author":"Petrie","year":"2010","journal-title":"GeoInformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1016\/j.measurement.2013.03.006","article-title":"Review of mobile mapping and surveying technologies","volume":"46","author":"Puente","year":"2013","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12497","DOI":"10.3390\/s130912497","article-title":"Data processing and quality evaluation of a boat-based mobile laser scanning system","volume":"13","author":"Vaaja","year":"2013","journal-title":"Sensors"},{"key":"ref_4","first-page":"223","article-title":"Rail track detection and modelling in mobile laser scanner data","volume":"1","author":"Khoshelham","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kaleli, F., and Akgul, Y.S. (2009, January 4\u20137). Vision-based railroad track extraction using dynamic programming. Proceedings of the 2009 IEEE 12th International Conference on Intelligent Transportation Systems, St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309526"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.3390\/rs6043075","article-title":"The use of airborne and mobile laser scanning for modeling railway environments in 3D","volume":"6","author":"Zhu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S40","DOI":"10.1016\/j.isprsjprs.2011.09.012","article-title":"Data fusion of extremely high resolution aerial imagery and lidar data for automated railroad centre line reconstruction","volume":"66","author":"Beger","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"25","article-title":"Extraction of railroad objects from very high resolution helicopterborne lidar and ortho-image data","volume":"38","author":"Neubert","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4750","DOI":"10.1109\/JSTARS.2014.2312378","article-title":"Automated extraction of 3-d railway tracks from mobile laser scanning point clouds","volume":"7","author":"Yang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.autcon.2010.06.007","article-title":"Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques","volume":"19","author":"Tang","year":"2010","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0924-2716(99)00004-0","article-title":"Two algorithms for extracting building models from raw laser altimetry data","volume":"54","author":"Maas","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","unstructured":"Verma, V., Kumar, R., and Hsu, S. (2006, January 17\u201322). 3D building detection and modeling from aerial lidar data. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA."},{"key":"ref_13","unstructured":"Rabbani, T., and Heuvel, F.V.D. (2004, January 17\u201319). Methods for fitting CSG models to point clouds and their comparison. Proceedings of the 7th IASTED International Conference on Computer Graphics and Imaging, Kauai, HI, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1177\/0278364910369190","article-title":"Object recognition in 3d point clouds using web data and domain adaptation","volume":"29","author":"Lai","year":"2010","journal-title":"Int. J. Robot. Res."},{"key":"ref_15","unstructured":"Diaz Benito, D. (2012). Automatic 3d Modelling of Train Rails in a Lidar Point Cloud. [M.Sc. Thesis, University of Twente]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4762","DOI":"10.1109\/JSTARS.2014.2309613","article-title":"A method for accurate road centerline extraction from a classified image","volume":"7","author":"Miao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/LGRS.2012.2214761","article-title":"Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines","volume":"10","author":"Miao","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"553","DOI":"10.5194\/isprsarchives-XL-5-553-2014","article-title":"Extracting rail track geometry from static terrestrail laser scans for monitoring purposes","volume":"XL-5","author":"Soni","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","first-page":"355","article-title":"Three-dimensional object recognition from single two-dimensional images","volume":"31","author":"Lowe","year":"1987","journal-title":"AI"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/j.0031-868X.2004.00290.x","article-title":"A model-based approach to semi-automated reconstruction of buildings from aerial images","volume":"19","author":"Khoshelham","year":"2004","journal-title":"Photogramm. Rec."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1093\/biomet\/57.1.97","article-title":"Monte carlo sampling methods using markov chains and their applications","volume":"57","author":"Hastings","year":"1970","journal-title":"Biometrika"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gilks, W.R., Richardson, S., and Spiegelhalter, D. (1995). Markov Chain Monte Carlo in Practice, Chapman and Hall\/CRC.","DOI":"10.1201\/b14835"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/5\/5565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:45:45Z","timestamp":1760215545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/5\/5565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,4]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2015,5]]}},"alternative-id":["rs70505565"],"URL":"https:\/\/doi.org\/10.3390\/rs70505565","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,5,4]]}}}