Vol. 2 No. 1
Articles

Evaluation of machine learning classifiers for 3D mobile LiDAR point cloud classification using different neighborhood search methods

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  • Mahmoud Mohamed,
  • Salem Morsy,
  • Adel El-Shazly
Mahmoud Mohamed
Fayoum University, Faculty of Engineering, Civil Engineering Department
Salem Morsy
Cairo University, Faculty of Engineering, Public Works Department
Adel El-Shazly
Cairo University, Faculty of Engineering, Public Works Department

Published 2022-03-30

Keywords

  • Mobile LiDAR,
  • Road Features,
  • Machine Learning,
  • Neighborhood Selection

How to Cite

Mohamed, M., Morsy, S., & El-Shazly , A. (2022). Evaluation of machine learning classifiers for 3D mobile LiDAR point cloud classification using different neighborhood search methods. Advanced LiDAR, 2(1), 01–09. Retrieved from https://publish.mersin.edu.tr/index.php/lidar/article/view/221

Abstract

Mobile LiDAR systems are distinguished with large and highly accurate point clouds data acquisition for road environments. Road features extraction is becoming one of the most important applications of LiDAR point cloud, and is used largely in road maintenance and autonomous driving vehicles. The main step in Mobile LiDAR processing is point classification This classification relies on the geometric definition of the points and their surroundings, as well as the classification methods used. The neighbors of each point is helpful to find more meaningful information than the raw coordinates. On the other hand, machine learning algorithms have proved their efficiency in LiDAR point cloud classification. This research compares results of using three machine learning classifiers, namely Random Forest, Gaussian Naïve Bayes, and Quadratic Discriminate Analysis along with using three neighborhood search methods, namely k nearest neighbors, spherical and cylindrical. A part of the pre-labelled benchmark dataset (Paris Lille 3D) with about 98 million points was tested. Results showed that the using Random Forest classifer with the cylindirical neighborhood search method acheived the highest overall accuracy of 92.39%.

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