Individual tree detection from airborne laser scanning data based on supervoxels and local convexity

Abstract

Precise mapping of urban green spaces is critical for sustainable development of urban ecosystem. LiDAR remote sensing technology has been proved to be valuable for capturing geometrical structure of natural and man-made resources. However, there has been no automatic and operational method to extract individual urban trees. This is largely due to the complexity involved in processing the massive LiDAR point cloud. It has been a challenge to label tree canopy point cloud compared to the point cloud from man-made structures such as buildings. This study proposes an object-based labeling framework for delineating individual tree canopy clusters in urban green spaces from airborne LiDAR point cloud. In addition, to reduce the computational complexity, supervoxels - a computer vision technique, has been applied as a pre-labeling process. The LiDAR point cloud is then semantically labeled using an object-based point cloud labeling framework. Experiments on an airborne LiDAR point cloud of a public park in Bergschen-hoek, Netherlands demonstrate the proposed methodology offers 99% accuracy when compared with the reference individual tree canopies data. The results also indicate the possibility of delineating multiple tree canopies which are overlapped, a situation which often leads to erroneous results from optical imagery. The proposed methodology flow results in a vector shape file as the output with individual tree locations categorized based on elevation.