  This paper describes preliminary work in the recent promising approach of
generating synthetic training data for facilitating the learning procedure of
deep learning (DL) models, with a focus on aerial photos produced by unmanned
aerial vehicles (UAV). The general concept and methodology are described, and
preliminary results are presented, based on a classification problem of fire
identification in forests as well as a counting problem of estimating number of
houses in urban areas. The proposed technique constitutes a new possibility for
the DL community, especially related to UAV-based imagery analysis, with much
potential, promising results, and unexplored ground for further research.
