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Retraction: Li et al. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39, doi.org/10.3390/ijgi8010039
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Open AccessArticle

Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud

by 1,2,* and 1,3
1
Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
2
LandCOBA, Digital Cartography Consulting Lda, Av. 5 de Outubro, 323, 1649-011 Lisboa, Portugal
3
Instituto Dom Luiz, Universidade de Lisboa, 1749-016 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 404; https://doi.org/10.3390/ijgi8090404
Received: 23 August 2019 / Revised: 31 August 2019 / Accepted: 9 September 2019 / Published: 11 September 2019
Road rutting caused by vehicle loading in the wheel path is a major form of asphalt pavement distress. Hydroplaning and loss of skid resistance are directly related to high road rutting severity. Periodical measurements of rut depth are crucial to maintenance and rehabilitation planning. In this study, we explored the feasibility of using point clouds gathered by Mobile LiDAR systems to measure the rut depth. These point clouds that are collected along roads are usually used for other purposes, namely asset inventory or topographic survey. Taking advantage of available clouds to identify rutting severity in critical pavement areas can result in considerable economic and time saving and thus, added value, when compared with specific expensive rut measuring systems. Four different strategies of cloud points aggregation are presented to create the cross-section of points. Such strategies were established to improve the precision of individual sensor measurements. Despite the 5 mm precision of the used system, it was possible to estimate rut depth values that were slightly inferior. The rut depth values obtained from each cross-section strategy were compared with the manual field measured values. The cross-sections based on averaged cloud points sensor profile aggregation was revealed to be the most suitable strategy to measure rut depth. Despite the fact that the study was specifically conducted to measure rut depth, the evaluation results show that the methodology can also be useful for other mobile LiDAR point clouds cross-sections applications. View Full-Text
Keywords: Mobile LiDAR systems; point clouds; LiDAR profiles; rutting; pavement distress Mobile LiDAR systems; point clouds; LiDAR profiles; rutting; pavement distress
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Gézero, L.; Antunes, C. Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud. ISPRS Int. J. Geo-Inf. 2019, 8, 404.

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