@inproceedings {26259,
	title = {Deep learning approaches for flood classification and flood aftermath detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	volume = {2283},
	year = {2018},
	publisher = {CEUR-WS.org},
	address = {Sophia Antipolis, France},
	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.},
	author = {Said, Naina and Pogorelov, Konstantin and Ahmad, Kashif and Riegler, Michael and Ahmad, Nasir and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola}
}
