Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods

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 street trees is required. In this research, an automatic method is proposed to detect single street trees from airborne Light Detection and Ranging (LiDAR) point cloud instead of traditional field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified to obtain high vegetation class 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. The accuracy assessment of the acquired street trees was also conducted using completeness and correctness analyses. The acquired results from urban study area approved the success of the proposed point-based approach for automatic detection of single street trees using LiDAR point cloud.

Applications of LiDAR in Agriculture and Future Research Directions

“…Ozdemir et al [ 58 ] utilised a data-preprocessing technique to extract trees from the LiDAR point cloud including the density-based spatial clustering of applications with noise (DBSCAN) algorithm [ 59 ] and cloth simulation filtering (CSF) method [ 60 ] for clustering and filtering, respectively. DBSCAN was also employed in [ 61 ] along with the mean shift algorithm for the automated detection of single street trees, where the ground truth data were derived through field investigations. They achieved high completeness and correctness values for two test areas and clustering methods.…”

Drainage Mechanisms of Submerged Sinkholes

“…Light Detection and Ranging (LiDAR) data have been increasingly used for classification of areas in recent decades [25][26][27]. In order to measure the evolution of the volume of the sinkhole and track local and regional deformations, five LiDAR scanning campaigns of the sinkhole and its vicinity were performed between June 2009 and The records are color-coded by the different instruments that were used during that period.…”