  Laser-scanned point clouds of forests make it possible to extract valuable
information for forest management. To consider single trees, a forest point
cloud needs to be segmented into individual tree point clouds. Existing
segmentation methods are usually based on hand-crafted algorithms, such as
identifying trunks and growing trees from them, and face difficulties in dense
forests with overlapping tree crowns. In this study, we propose TreeLearn, a
deep learning-based approach for tree instance segmentation of forest point
clouds. Unlike previous methods, TreeLearn is trained on already segmented
point clouds in a data-driven manner, making it less reliant on predefined
features and algorithms. Furthermore, TreeLearn is implemented as a fully
automatic pipeline and does not rely on extensive hyperparameter tuning, which
makes it easy to use. Additionally, we introduce a new manually segmented
benchmark forest dataset containing 156 full trees, and 79 partial trees, that
have been cleanly segmented by hand. The data is generated by mobile laser
scanning and contributes to create a larger and more diverse data basis for
model development and fine-grained instance segmentation evaluation. We trained
TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360
software. An evaluation on the benchmark dataset shows that TreeLearn performs
equally well or better than the algorithm used to generate its training data.
Furthermore, the method's performance can be vastly improved by fine-tuning on
the cleanly labeled benchmark dataset. The TreeLearn code is available from
https://github.com/ecker-lab/TreeLearn. The data as well as trained models can
be found at https://doi.org/10.25625/VPMPID.
