Image-based Road Marking Classification and Vector Data Derivation from Mobile Mapping 3D Point Clouds

Johannes Wolf, Tobias Pietz, Rico Richter, Sören Discher, Jürgen Döllner

Abstract

Capturing urban areas and infrastructure for automated analysis processes becomes ever more important. Laserscanning and photogrammetry are used for scanning the environment in highly detailed resolution. In this work, we present techniques for the semantic classification of 3D point clouds from mobile mapping scans of road environments and the detection of road markings. The approach renders 3D point cloud input data into images for which U-Net as an established image recognition convolutional neural network is used for the semantic classification. The results of the classification are projected back into the 3D point cloud. An automated extraction of vector data is applied for detected road markings, generating detailed road marking maps. Different approaches for the vector data generation are used depending on the type of road markings, such as arrows or dashed lines. The automatically generated shape files created by the presented process can be further used in various GIS applications. Our results of the implemented out-of-core techniques show that the approach can efficiently be applied on large datasets of entire cities.

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Paper Citation


in Harvard Style

Wolf J., Pietz T., Richter R., Discher S. and Döllner J. (2021). Image-based Road Marking Classification and Vector Data Derivation from Mobile Mapping 3D Point Clouds.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 227-234. DOI: 10.5220/0010190602270234


in Bibtex Style

@conference{visapp21,
author={Johannes Wolf and Tobias Pietz and Rico Richter and Sören Discher and Jürgen Döllner},
title={Image-based Road Marking Classification and Vector Data Derivation from Mobile Mapping 3D Point Clouds},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={227-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010190602270234},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Image-based Road Marking Classification and Vector Data Derivation from Mobile Mapping 3D Point Clouds
SN - 978-989-758-488-6
AU - Wolf J.
AU - Pietz T.
AU - Richter R.
AU - Discher S.
AU - Döllner J.
PY - 2021
SP - 227
EP - 234
DO - 10.5220/0010190602270234