Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

Mi, Xiaoxin; Wang, Jinhu; Chen, Chi; Gu, Jiande

doi:10.1109/tits.2021.3052882

Cited by 27 publications

(16 citation statements)

References 30 publications

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“…Mi et al [ 15 ] classified road boundary extraction methods into three categories, such as activity-driven, feature-driven, and model-driven, although methods are also grouped based sensor data, such as LiDAR or optical imagery. Activity-driven methods take advantage of vehicle motion (trajectory information) to classify road and non-road regions [ 16 ].…”

Section: Related Studiesmentioning

confidence: 99%

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

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Section: Related Studiesmentioning

confidence: 99%

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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“…Finally, the resulting voxel grid is transformed into an elevation map where the road curb detection process takes place. The authors of [56] used supervoxels to extract road curb candidates from ground-segmented point clouds. Supervoxel candidates are then processed through clustering and other refinements to generate vectorized road boundaries, see Figure 11.…”

Section: Voxel Gridsmentioning

confidence: 99%

“…In [46], a height difference is computed using an estimation of the height difference which is computed using a trapezoidal rule of integration. Height difference on a super voxel grid was used in [56]. A similar feature has been applied on the projected surface or plane where the horizontal distance between consecutive points is computed as in [65].…”

Section: Height Stepmentioning

confidence: 99%

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Road Curb Detection: A Historical Survey

Romero

Guerrero

Romero

2021

Sensors

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Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.

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“…High-spatial-resolution remote sensing data have shown great potential for application in areas such as precision agricultural monitoring [1][2][3], urban and rural regional planning, road traffic management [4,5], high precision navigation maps [6][7][8], environmental disaster assessment [9,10], forestry measurement [11][12][13], and military construction. Buildings, as the main body in urban construction, occupy a more important component in highresolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

Wang,

Lin,

Huang

et al. 2023

Remote Sensing

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Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.

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Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

Cited by 27 publications

References 30 publications

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Road Curb Detection: A Historical Survey

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

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