  Remote sensing through unmanned aerial systems (UAS) has been increasing in
forestry in recent years, along with using machine learning for data
processing. Deep learning architectures, extensively applied in natural
language and image processing, have recently been extended to the point cloud
domain. However, the availability of point cloud datasets for training and
testing remains limited. Creating forested environment point cloud datasets is
expensive, requires high-precision sensors, and is time-consuming as manual
point classification is required. Moreover, forest areas could be inaccessible
or dangerous for humans, further complicating data collection. Then, a question
arises whether it is possible to use synthetic data to train deep learning
networks without the need to rely on large volumes of real forest data. To
answer this question, we developed a realistic simulator that procedurally
generates synthetic forest scenes. Thanks to this, we have conducted a
comparative study of different state-of-the-art point-based deep learning
networks for forest segmentation. Using created datasets, we determined the
feasibility of using synthetic data to train deep learning networks to classify
point clouds from real forest datasets. Both the simulator and the datasets are
released as part of this work.
