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

arXiv: https://arxiv.org/abs/1908.06472 · HTML: https://mlsanity.com/paper/1908.06472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [EscapeWildFire: Assisting People to Escape Wildfires in Real-Time](https://mlsanity.com/paper/2102.11558/) (2021)

## Abstract (arXiv)

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.