Geometric Feature Extraction of Road from UAV Based Point Cloud Data

Zeybek, Mustafa; Biçici, Serkan

doi:10.1007/978-3-030-66840-2_33

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…Homayounfar N et al [ 27 ] developed a hierarchical recurrent network and a new differentiable loss function to solve the problem of extracting road networks from sparse 3D point clouds. Zeybek M et al [ 28 ] achieved automatic extraction of roads using point clouds and machine learning classification methods, and used point clouds to create digital elevation models to extract profile and section elevations, enabling automatic extraction of high-precision road geometric features from unmanned aerial vehicle (UAV) images.…”

Section: Road Generationmentioning

confidence: 99%

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Zhu¹,

Wang²,

Meng³

et al. 2022

Sensors

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The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized.

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Section: Road Generationmentioning

confidence: 99%

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Zhu¹,

Wang²,

Meng³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Roads are one of the fundamental urban characteristics and serve numerous different applications in practice. For example, the rapid and practical extraction of the road centerline can be used for navigation, route planning and inventory, and traffic planning (39,40). The detection of damage and distress on roads can be inspected on the extracted road surface.…”

Section: Literature Reviewmentioning

confidence: 99%

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

Biçici

Zeybek

2021

Transportation Research Record

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The accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%–13%, and 95% overall accuracy was obtained when using convenient training samples and features.

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“…Then, road surfaces were extracted manually from these four-point clouds. There are several methods to automatically extract the road surface from the point cloud (Zeybek and Biçici, 2021). However, this study does not focus on the automatic extraction of the road.…”

Section: Image Processingmentioning

confidence: 99%

“…The development of smaller and lower cost sensors such as sensors on unmanned aerial vehicle (UAV) is also seen as the potential of these surveying tools to develop 3D data with high resolution and accuracy. Therefore, UAV is one of the fastest-growing technologies used in many disciplines (Biçici and Zeybek, 2021;Zeybek and Biçici, 2021). In this method, images taken from UAV flights are converted into 3D data using Structure-from-Motion (SfM) based software.…”

Section: Introductionmentioning

confidence: 99%

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

Zeybek¹,

Biçici²

2021

Turkish Journal of Remote Sensing and GIS

Self Cite

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Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, it was found that the Agisoft Metashape software produced more accurate and faster results than Pix4D Mapper software.

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Geometric Feature Extraction of Road from UAV Based Point Cloud Data

Cited by 9 publications

References 29 publications

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

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