{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:59:03Z","timestamp":1770753543834,"version":"3.50.0"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T00:00:00Z","timestamp":1544400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Research and Development Program of China","award":["Grant No. 2017YFB0503401, Grant No. 2016YFB0501802"],"award-info":[{"award-number":["Grant No. 2017YFB0503401, Grant No. 2016YFB0501802"]}]},{"name":"National Natural Science Funds of China","award":["Grant No. 41704026, Grant No. 41374034, Grant No. 41404010"],"award-info":[{"award-number":["Grant No. 41704026, Grant No. 41374034, Grant No. 41404010"]}]},{"name":"Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing","award":["Grant No. 15P01"],"award-info":[{"award-number":["Grant No. 15P01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck\/onboard GNSS receiver\/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required.<\/jats:p>","DOI":"10.3390\/rs10121998","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T11:31:16Z","timestamp":1544441476000},"page":"1998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Yidong","family":"Lou","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Tian","family":"Zhang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Jian","family":"Tang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Weiwei","family":"Song","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, X., Lang, M., Gao, Y., Wang, K., Su, C., Tsai, S., Huo, M., Yu, X., and Li, S. 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