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

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Kamilaris Andreas</dc:creator>
  <dc:creator>van den Brink Corjan</dc:creator>
  <dc:creator>Karatsiolis Savvas</dc:creator>
  <dc:description>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.</dc:description>
  <dc:description>This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</dc:description>
  <dc:publisher>Springer International Publishing</dc:publisher>
  <dc:subject>UAV  Deep Learning</dc:subject>
  <dc:subject>Generative Data</dc:subject>
  <dc:subject>Aerial Imagery</dc:subject>
  <dc:title>Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</dc:title>
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