Tree detection from airborne LiDAR data using image processing and 3D deep learning

Abstract

Trees in urban areas play important roles such as alleviating heat island effect, absorbing carbon dioxide, and offering bio-diversity. Tree monitoring is, first of all, crucial to effectively manage and utilize the trees. Many efforts have been done to monitor the tree structure using image analysis. For example, tree species classification and above ground biomass estimation were conducted using satellite image analysis. Tree measurement was also performed using laser scanner called LiDAR mounted on airplane, helicopter and drone to obtain the spatial information. It has been reported that the tree structure such as tree height and crown volume can be estimated from the LiDAR point cloud. To utilize the LiDAR data widely, automatic detection in the 3D point cloud is required. Trees in 3D point cloud can be detected using, for example, watershed and valleyfollowing method. Additionally, high classification and object detection accuracy could be performed using deep learning-based technique. In this study, we combined image processing and 3D deep learning technique to automatically detect trees in 3D point cloud obtained from airborne LiDAR.