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Classification of Mobile Laser Scanning Point Cloud in an Urban Environment Using kNN and Random Forest

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Innovations in Smart Cities Applications Volume 5 (SCA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 393))

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Abstract

This study presents the supervised classification of point clouds collected through a vehicle-based Mobile Laser Scanning (MLS) system in an urban environment. A benchmark dataset representing the Technical University of Munich (TUM) City Campus was utilized. The main contribution of this paper is to reveal the performance difference between the 2D and 3D k-nearest neighbor (kNN) information on the point-based classification of an MLS system using local geometric and eigen-based features. Random Forest (RF) classifier is utilized for the supervised classification of 8 classes predefined in the benchmark set: artificial terrain, natural terrain, high vegetation, low vegetation, building, hardscape, artifact, and vehicle. As a result, we achieve an impressive classification performance using the 2D kNN information. We reveal that 2D kNN with 12 features not only improves the processing speed but also provides an improvement of 7.3% compared to the 3D kNN, and at least 2.8% overall accuracy improvement compared to the state-of-the-art approaches previously developed and tested using the benchmark.

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Acknowledgements

The authors would like to thank Dr. Marcus Hebel and Joachim Gehrung for providing the datasets. The original MLS dataset is copyrighted by Fraunhofer IOSB and it is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

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Authors and Affiliations

  1. Department of Geomatics Engineering, Hacettepe University, Ankara, Turkey

    Semanur Seyfeli & Ali Ozgun Ok

Authors
  1. Semanur Seyfeli
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  2. Ali Ozgun Ok
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Corresponding author

Correspondence to Semanur Seyfeli .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques, Computer Engineering Department, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Faculty of Sciences and Techniques, Computer Engineering Department, Abdelmalek Essaadi University, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. Computer Engineering Department, Karabük University, Karabük, Turkey

    İsmail Rakıp Karaș

  4. Operations and Supply Chain Management, Wellington School of Business and Govern, Wellington, Wellington, New Zealand

    Vipul Jain

  5. Département Des Systèmes d'Information Organisationnels, Faculty of Business Administration. Laval University, Québec, QC, Canada

    Sehl Mellouli

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Seyfeli, S., Ok, A.O. (2022). Classification of Mobile Laser Scanning Point Cloud in an Urban Environment Using kNN and Random Forest. In: Ben Ahmed, M., Boudhir, A.A., Karaș, İ.R., Jain, V., Mellouli, S. (eds) Innovations in Smart Cities Applications Volume 5. SCA 2021. Lecture Notes in Networks and Systems, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-94191-8_78

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  • DOI: https://doi.org/10.1007/978-3-030-94191-8_78

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94190-1

  • Online ISBN: 978-3-030-94191-8

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