  Humans use UAVs to monitor changes in forest environments since they are
lightweight and provide a large variety of surveillance data. However, their
information does not present enough details for understanding the scene which
is needed to assess the degree of deforestation. Deep learning algorithms must
be trained on large amounts of data to output accurate interpretations, but
ground truth recordings of annotated forest imagery are not available. To solve
this problem, we introduce a new large aerial dataset for forest inspection
which contains both real-world and virtual recordings of natural environments,
with densely annotated semantic segmentation labels and depth maps, taken in
different illumination conditions, at various altitudes and recording angles.
We test the performance of two multi-scale neural networks for solving the
semantic segmentation task (HRNet and PointFlow network), studying the impact
of the various acquisition conditions and the capabilities of transfer learning
from virtual to real data. Our results showcase that the best results are
obtained when the training is done on a dataset containing a large variety of
scenarios, rather than separating the data into specific categories. We also
develop a framework to assess the deforestation degree of an area.
