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

## TL;DR

This paper explores generating synthetic aerial images from UAVs to train deep learning models, demonstrating promising initial results in fire detection and urban counting tasks, and highlighting new research opportunities.

## Contribution

It introduces a novel approach of using synthetic data for UAV imagery analysis, addressing data scarcity and enabling improved deep learning model training.

## Key findings

- Preliminary results show effective fire classification.
- Synthetic data aids in urban house counting.
- Method offers new research directions for UAV-based deep learning.

## 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.