Improvements on Road Centerline Extraction by Combining Voronoi Diagram and Intensity Feature from 3D UAV-Based Point Cloud

Biçici, Serkan; Zeybek, Mustafa

doi:10.1007/978-3-030-94191-8_76

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…For example, Mohan (2021) [32] has developed a fully automated highway inspection framework based on a convolutional neural network using a novel low-power drone. Biçici (2021) [33] has proposed a method for road classification and extraction from a UAV point cloud based on random forest and further combines it with the Voronoi diagram to improve the quality of road centerline extraction [34]. These techniques make ULS data ideal for road safety assessment.…”

Section: Lidar Measurement Technologymentioning

confidence: 99%

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

et al. 2023

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The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters.

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Section: Lidar Measurement Technologymentioning

confidence: 99%

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

et al. 2023

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“…There are different methods and approaches to classify road surfaces from 3D data. A machine learning (ML) algorithm is one of the widely used methods [18][19][20]. Numerous ML algorithms have been introduced in the literature [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…The training samples and the ML algorithm are two other essential elements in classifying road surfaces, and previous studies have investigated their effects [18,23,25]. For instance, poorly constructed training samples can lead to inaccurate classification.…”

Section: Introductionmentioning

confidence: 99%

Different KNN parameters on road surface extraction from UAV-based point cloud under several machine learning algorithms

Biçici

2024

Meas. Sci. Technol.

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Accurate road surface from a three-dimensional (3D) point cloud depends on various parameters. One crucial parameter is the set of point features. Point features enable classification by capturing characteristics of the surface on which the points are located. These features are calculated based on the closest points surrounding each point. In this study, the K-nearest neighbors algorithm (KNN) was applied to identify these closest points. The KNN algorithm requires only one input, the number of closest points (k). Eight different point features were developed using different k values, and their impact on road surface classification from the 3D point cloud was investigated. It was observed that there is no significant improvement in classification accuracy until a certain k value. However, better classification accuracy was achieved after a certain k value. The effect of different k values was also investigated under different training sample structures and machine learning (ML) algorithms. When training samples were selected from a single location as a large group, similar classification accuracy was obtained across different k values. Conversely, when training samples were chosen from various regions in smaller groups rather than a single large group, improved classification was observed as the k value increased. Additionally, it was noted that five different ML algorithms-random forest, support vector machine, generalized linear model, linear discriminant analysis, and robust linear discriminant analysis-have almost similar performance under different k values. Finally, using the optimum k value, improvements of up to 4.543% and 6.601% in accuracy and quality measures, respectively, were found.

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Investigation of landslide-based road surface deformation in mountainous areas with single period UAV data

Zeybek

Biçici

2022

Geocarto International

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Improvements on Road Centerline Extraction by Combining Voronoi Diagram and Intensity Feature from 3D UAV-Based Point Cloud

Cited by 3 publications

References 21 publications

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

Different KNN parameters on road surface extraction from UAV-based point cloud under several machine learning algorithms

Investigation of landslide-based road surface deformation in mountainous areas with single period UAV data

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