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Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization

  • Surveying and Geo-Spatial Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Applying point cloud data to road analysis is crucial for obtaining practical features for segmenting and classifying road point clouds. This study proposes a multi-step method for extracting road points and road network structures from urban Light Detection and Ranging (LiDAR) point cloud data. The first step is a two-step algorithm of coarse grid classification and local optimization of point cloud fine classification. This step extracts road point clouds from various parts of the city. The second step involves the road-point cloud splicing work. Finally, we extract the urban road network structure according to the point cloud of the urban main road and calculate the width of each road. We evaluate the method’s feasibility using four urban road point clouds. Experimental results show that the proposed method can quickly and accurately extract road points, obtaining a road data accuracy and integrity of >94% and a road width estimated relative error of <7%.

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Acknowledgments

This work was supported by the National College Students innovation and entrepreneurship training program (No. X202210022200).

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Authors and Affiliations

  1. College of Science, Beijing Forestry University, Beijing, 100083, China

    Xuanzhen Liu & Hongjun Li

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  1. Xuanzhen Liu
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  2. Hongjun Li
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Correspondence to Hongjun Li.

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Liu, X., Li, H. Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization. KSCE J Civ Eng 28, 354–362 (2024). https://doi.org/10.1007/s12205-023-0919-x

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  • DOI: https://doi.org/10.1007/s12205-023-0919-x

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