Classification of Mobile Laser Scanning Data with Geometric Features and Cylindrical Neighborhood


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Seyfeli S., OK A. Ö.

BALTIC JOURNAL OF MODERN COMPUTING, vol.10, no.2, pp.209-223, 2022 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.22364/bjmc.2022.10.2.08
  • Journal Name: BALTIC JOURNAL OF MODERN COMPUTING
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.209-223
  • Keywords: Mobile Laser Scanning, Feature Extraction, Geometric Feature, Cylindrical Neighborhood, LIDAR, MULTISCALE
  • Hacettepe University Affiliated: Yes

Abstract

Mobile laser scanning (MLS) is a favored information source for urban areas because of its capacity to gather quick and dense three-dimensional information, and classification studies of these large point clouds are carried out by enriching the data set collected. This study discusses a method for classifying an MLS point cloud by constructing a local neighborhood relationship with cylindrical neighborhood information and augmenting the dataset with geometric features in the absence of RGB images. Two benchmark datasets, TUM-MLS1 and Toronto-3D, were employed. The Random Forest (RF) classifier, which has been preferred in many researches for MLS classification, was built and assessed on eight classes of benchmark datasets for point-based supervised classification. As a result, we achieved 94.5% overall accuracy with only four geometric features for both datasets. When comparing our findings for dataset of TUM-MLS1 to those of a previous study, we found a 2.4% increase in overall accuracy.