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International Conference on Discrete Geometry and Mathematical Morphology

DGMM 2021: Discrete Geometry and Mathematical Morphology pp 93–106Cite as

Automatic Forest Road Extraction from LiDAR Data of Mountainous Areas

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12708)

Abstract

In this paper, a framework is proposed to extract forest roads from LiDAR (Light Detection and Ranging) data in mountainous areas. For that purpose, an efficient and simple solution based on discrete geometry and mathematical morphology tools is proposed. The framework is composed of two steps: (i) detecting road candidates in DTM (Digital Terrain Model) views using a mathematical morphology filter and a fast blurred segment detector in order to select a set of road seeds; (ii) extracting road sections from the obtained seeds using only the raw LiDAR points to cope with DTM approximations. For the second step, a previous tool for fast extraction of linear structures directly from ground points was adapted to automatically process each seed. It first performs a recognition of the road structure under the seed. In case of success, the structure is tracked and extended as far as possible on each side of the segment before post-processing validation and cleaning. Experiments on real data over a wide mountain area (about 78 km\(^2\)) have been conducted to validate the proposed method.

Keywords

  • LiDAR data
  • Road detection
  • Point cloud processing
  • DTM image analysis

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References

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Acknowledgements

This work was realized in the scope of SolHoM interdisciplinary project of Université de Lorraine. LiDAR data were acquired in the scope of PCR AGER project (Projet collectif de recherche – Archéologie et GEoarchéologie du premier Remiremont et de ses abords), and left available to SolHoM project.

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Authors and Affiliations

  1. Université de Lorraine, CNRS, LORIA, Nancy, 54000, France

    Philippe Even & Phuc Ngo

Authors
  1. Philippe Even
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  2. Phuc Ngo
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Corresponding author

Correspondence to Philippe Even .

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Editors and Affiliations

  1. Uppsala University, Uppsala, Sweden

    Joakim Lindblad

  2. Uppsala University, Uppsala, Sweden

    Dr. Filip Malmberg

  3. Uppsala University, Uppsala, Sweden

    Dr. Nataša Sladoje

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Even, P., Ngo, P. (2021). Automatic Forest Road Extraction from LiDAR Data of Mountainous Areas. In: Lindblad, J., Malmberg, F., Sladoje, N. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2021. Lecture Notes in Computer Science(), vol 12708. Springer, Cham. https://doi.org/10.1007/978-3-030-76657-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-76657-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76656-6

  • Online ISBN: 978-3-030-76657-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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