  Path loss prediction for wireless communications is highly dependent on the
local environment. Propagation models including clutter information have been
shown to significantly increase model accuracy. This paper explores the
application of deep learning to satellite imagery to identify environmental
clutter types automatically. Recognizing these clutter types has numerous uses,
but our main application is to use clutter information to enhance propagation
prediction models. Knowing the type of obstruction (tree, building, and further
classifications) can improve the prediction accuracy of key propagation metrics
such as path loss.
