Segmentation of 3D point cloud data from trees

Segmentation of 3D point cloud data from trees

3D data from mobile laser scans can be collected quickly and easily for entire forest areas and thus digital images of the forest stands can be generated. An ongoing challenge has been the automatic segmentation of such point clouds into individual trees. Only a few algorithms exist for this purpose, and they do not perform well in all cases. The goal of this project is to develop an automated tree segmentation using methods from deep learning.

tree point clouds

Figure 1: Example of a mobile 3D scan of a beech forest.


  • Good mathematical understanding (in particular statistics and linear algebra)
  • Interest in 3D data and good spatial awareness
  • Python programming
  • Experience with deep learning (PyTorch or Tensorflow recommended)


Alexander Ecker and Dominik Seidel (Dept. Silviculture and Forest Ecology of the Temperate Zones)

Neural Data Science Group
Institute of Computer Science
University of Goettingen