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arXiv · 1908.06472

Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Andreas Kamilaris·Corjan van den Brink·Savvas Karatsiolis
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Abstract

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.

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BibTeXRIS

Andreas Kamilaris, Corjan van den Brink, Savvas Karatsiolis. 2019-08-18. Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles. https://arxiv.org/abs/1908.06472

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