Conference paper Embargoed 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.
Embargoed Access
Files are currently under embargo but will be publicly accessible after October 30, 2020.
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| Data volume | 10.4 MB | 10.4 MB |
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| Unique downloads | 6 | 6 |
- 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.
- Communities:
- License (for files):
- Creative Commons Attribution Non Commercial No Derivatives 4.0 International