  LiDAR provides highly accurate 3D point clouds. However, data needs to be
manually labelled in order to provide subsequent useful information. Manual
annotation of such data is time consuming, tedious and error prone, and hence
in this paper we present three automatic methods for annotating trees in LiDAR
data. The first method requires high density point clouds and uses certain
LiDAR data attributes for the purpose of tree identification, achieving almost
90% accuracy. The second method uses a voxel-based 3D Convolutional Neural
Network on low density LiDAR datasets and is able to identify most large trees
accurately but struggles with smaller ones due to the voxelisation process. The
third method is a scaled version of the PointNet++ method and works directly on
outdoor point clouds and achieves an F_score of 82.1% on the ISPRS benchmark
dataset, comparable to the state-of-the-art methods but with increased
efficiency.
