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A platform for research: civil engineering, architecture and urbanism
Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization
https://orcid.org/https://orcid.org/0009-0009-9825-4175
https://orcid.org/https://orcid.org/0000-0003-1102-6225
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%.
Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization
https://orcid.org/https://orcid.org/0009-0009-9825-4175
https://orcid.org/https://orcid.org/0000-0003-1102-6225
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%.
Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization
https://orcid.org/https://orcid.org/0009-0009-9825-4175
https://orcid.org/https://orcid.org/0000-0003-1102-6225
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%.
Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization
KSCE J Civ Eng
Liu, Xuanzhen (author) / Li, Hongjun (author)
KSCE Journal of Civil Engineering ; 28 ; 354-362
2024-01-01
9 pages
Article (Journal)
Electronic Resource
English
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