Abstract: The airborne lidar scanning (ALS) system provides the possibility of acquiring large-scale tree point clouds
only from a single scan, which helps to achieve the structural parameter extraction of trees and geometric
reconstruction at the landscape level with higher accuracy. However, it remains a challenge to accurately segment and
model trees from ALS point clouds, due to the diversity of tree species and the complex topology of trees. Although
the traditional point cloud-based automatic tree segmentation and modeling algorithm are efficient, there remain such
problems as great segmentation errors and lack of robustness for the modeling algorithm, making it difficult to meet users’ need for the precise annotation of tree segmentation and of modeling results in deep learning. In order to solve
the problems of automatic segmentation and modeling caused by the sparse and incomplete ALS point clouds, an
interactive hierarchical segmentation method was proposed based on height mapping. The proposed method can
extract a single tree point cloud from sparse point clouds, and then utilize the improved space colonization algorithm
(SCA) to model trees by interactively adjusting the parameters of constraint angle, kill distance, and influence radius.
Experimental results show that the proposed interactive segmentation algorithm can avoid the false segmentation
arising from the minimum spanning tree and Normalized-Cut algorithm. The proposed interactive modeling algorithm
can robustly generate tree models from the sparse and incomplete ALS point clouds, as well as preserving the features
of the original point cloud by selecting the appropriate combination of SCA parameters.

Key words: airborne lidar scanning, tree, point cloud, segmentation, modeling