  This paper presents an autonomous approach to tree detection and segmentation
in high resolution airborne LiDAR that utilises state-of-the-art region-based
CNN and 3D-CNN deep learning algorithms. If the number of training examples for
a site is low, it is shown to be beneficial to transfer a segmentation network
learnt from a different site with more training data and fine-tune it. The
algorithm was validated using airborne laser scanning over two different
commercial pine plantations. The results show that the proposed approach
performs favourably in comparison to other methods for tree detection and
segmentation.
