@misc{15157,
  author = {Naina Said and Konstantin Pogorelov and Kashif Ahmad and Michael Riegler and Nasir Ahmad and Olga Ostroukhova and P{\r a}l Halvorsen and Nicola Conci},
  title = {Deep learning approaches for flood classification and flood aftermath detection},
  abstract = {This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events. In the first challenge, we mainly rely on object and scene level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are combined using early, late and double fusion techniques achieving an average F1-score of 65.03\%, 60.59\% and 63.58\%, respectively. For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30\% and 61.02\% for run 1 and run 2, respectively.},
  year = {2018},
  journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
  volume = {2283},
  publisher = {CEUR-WS.org},
  address = {Sophia Antipolis, France},
}