AUTOMATIC DETECTION OF SINGLE STREET TREES FROM AIRBORNE LiDAR DATA BASED ON POINT SEGMENTATION METHODS


Çetin Z. , Yastıklı N.

International Symposium on Applied Geoinformatics (ISAG 2021), Riga, Latvia, 2 - 03 December 2021, pp.1-5

  • Publication Type: Conference Paper / Summary Text
  • City: Riga
  • Country: Latvia
  • Page Numbers: pp.1-5

Abstract

As a primary element of urban ecosystem, street trees are very essential for environmental quality and aesthetic beauty of urban landscape. Street trees play a crucial role in everyday life of city inhabitants and therefore, comprehensive and accurate inventory information for planning and maintenance of street trees are required. In this study, an automatic method is proposed to detect single street trees using airborne Light Detection and Ranging (LiDAR) data instead of traditional field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified into ground, low vegetation, medium vegetation, high vegetation, building, low point and air point classes with a hierarchical rule-based classification method. Then, the LiDAR points in high vegetation class were segmented with Mean Shift and Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to acquire single urban street trees in the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey as study area. The accuracy assessment of the acquired street trees was also conducted using completeness and correctness analyses.