Conference paper Open Access
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles
Kamilaris Andreas;
van den Brink Corjan;
Karatsiolis Savvas
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 classication problem of re identication 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.
| Name | Size | |
|---|---|---|
|
pre_print.pdf
md5:2e8ab4fd5be9f9721d9967a058e66477 |
1.3 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 52 | 52 |
| Downloads | 16 | 16 |
| Data volume | 20.8 MB | 20.8 MB |
| Unique views | 47 | 47 |
| Unique downloads | 14 | 14 |
- Publication date:
- October 30, 2019
- DOI:
-
- Keyword(s):
- UAV Deep Learning Generative Data Aerial Imagery
- Published in:
- Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089, Springer International Publishing, Cham, pp. 81-90.
- Grants:
-
European Commission:
- RISE - Research Center on Interactive Media, Smart System and Emerging Technologies (739578)
- Communities:
- License (for files):
- Creative Commons Attribution Non Commercial No Derivatives 4.0 International