A DATASET FOR INDIVIDUAL TREE DELINEATION FROM 3D POINT CLOUD DATA

LiDAR scanning data, which is able to acquire the vertical structures of forests, is of great potential in forest monitoring and biodiversity quantification. Besides, the derivation of some forest indices, such as biomass, relies on individual tree delineation (ITD). In this paper, we generated a dataset for individual tree delineation using LiDAR-derived point clouds. This dataset can be used to fairly compare different ITD methods and to develop deep learning algorithms for tree segmentation. The acquired LiDAR data consist of 0.94 billion points covering an area of about 31 km2 in the Netherlands. We first used a rule-based algorithm to remove non-tree points. And then a mean shift clustering method is utilized to segment the points. Besides, we proposed a method that compares the highest point in the same cluster to evaluate the delineation results. In the future, the derived segmentation result will be compared with existing individual tree delineation algorithms.