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Retraction published on 11 September 2019, see ISPRS Int. J. Geo-Inf. 2019, 8(9), 402.
Open AccessArticle

Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

1
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
2
College of Engineering, Peking University, Beijing 100871, China
3
Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
4
Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
5
Zhengzhou Institute of Surveying and Mapping, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 39; https://doi.org/10.3390/ijgi8010039
Received: 22 December 2018 / Accepted: 13 January 2019 / Published: 16 January 2019
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance. View Full-Text
Keywords: UAV; LiDAR; asphalt pavement distresses; pavement health conditions; multiscale features; random forest classification UAV; LiDAR; asphalt pavement distresses; pavement health conditions; multiscale features; random forest classification
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MDPI and ACS Style

Li, Z.; Cheng, C.; Kwan, M.-P.; Tong, X.; Tian, S. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39.

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