  For many countries like Russia, Canada, or the USA, a robust and detailed
tree species inventory is essential to manage their forests sustainably. Since
one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to
large-scale forest inventory applications, the utilization of machine learning
algorithms on satellite imagery is a rising topic of research. Although
satellite imagery quality is relatively low, additional spectral channels
provide a sufficient amount of information for tree crown classification tasks.
Assuming that tree crowns are detected already, we use embeddings of tree
crowns generated by Autoencoders as a data set to train classical Machine
Learning algorithms. We compare our Autoencoder (AE) based approach to
traditional convolutional neural networks (CNN) end-to-end classifiers.
