Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model
Mustafa Zeybek, Serkan Biçici
Abstract
Abstract Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the mobile LiDAR scanning raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>97</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>99</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> . When comparing these measures with previous studies, the proposed method produces one of the highest ones.