Automated extraction of expressway road surface from mobile laser scanning data

Tran, Thanh Ha; Chaisomphob, Taweep

doi:10.1007/s11771-020-4420-0

Cited by 12 publications

(15 citation statements)

References 45 publications

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“…Subsequently the Random Sample Consensus (RANSAC) method was employed for ground point extraction by determining the average height of the ground points. Tran et al [26] presented a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment. However, these methods were designed for relatively flat and simple road scenes.…”

Section: Related Workmentioning

confidence: 99%

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

Zhao,

Ning,

Jia

et al. 2024

Applied Sciences

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Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.

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Section: Related Workmentioning

confidence: 99%

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

Zhao,

Ning,

Jia

et al. 2024

Applied Sciences

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“…A mobile terrestrial laser scanner can record a high‐density three‐dimensional (3D) point cloud from the perspective of a vehicle travelling at highway speeds (Wang et al, 2019). This technology has been used in various roadway asset management applications including extracting and identifying trees (Huang et al, 2020; Husain & Chandra Vaishya, 2020; Safaie et al, 2021), lane markings (Ma et al, 2020; Rastiveis et al, 2020), road signs (Guan et al, 2020; Soilán et al, 2016), utility poles and power lines (Liu et al, 2020; Thanh Ha & Chaisomphob, 2020; Tu et al, 2020), roads attributes (Ma, 2020; Tran & Taweep, 2020), and building facades (Xia & Wang, 2019).…”

Section: Introductionmentioning

confidence: 99%

An enhanced descriptor extraction algorithm for power line detection from point clouds

Shokri

Rastiveis

Sarasua

et al. 2023

Geographical Research

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Mobile terrestrial laser scanning (MTLS) systems provide a safe and efficient means to survey roadway corridors at high speed. MTLS point clouds are rich in planimetric data. However, manual extraction of useful information from these point clouds can be time consuming and laborious and automated object extraction from MTLS point clouds has become a hot topic in the remote sensing community. This study proposes an automated method for power line extraction from MTLS point clouds based on a multilayer perceptron (MLP) neural network. The proposed method consists of three main steps: (i) point cloud preprocessing, (ii) descriptor extraction and selection, and (iii) point classification. The preprocessing step involves filtering out more than 90% of the point cloud by eliminating the vast majority of unneeded points. Next, various descriptors are extracted from the remaining points including planarity, linearity, and verticality, and the descriptor standard deviation is used to select the best‐suited descriptors for power line extraction. Finally, an MLP neural network is trained using the selected descriptors from several cable and noncable sample points. The proposed algorithm was evaluated in three MTLS point clouds in urban and nonurban environments totalling 5.5 kilometres in length. An average precision of 94% and a recall of 94% showed the algorithm’s reliability and feasibility.

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“…Díaz-Vilariño et al [26] developed a method for automatic classification of asphalt and pavement for mobile LiDAR point cloud. The road surface of a point cloud was extracted using a voxel-based and pavement-based segmentation algorithm in another study [27]. In addition, the advantage of the airborne LiDAR in extracting the road surface was also discussed in several studies [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

Zeybek¹,

Biçici²

2023

Meas. Sci. Technol.

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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 MLS 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 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.

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Automated extraction of expressway road surface from mobile laser scanning data

Cited by 12 publications

References 45 publications

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

An enhanced descriptor extraction algorithm for power line detection from point clouds

Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

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