|Authors||N. Said, K. Pogorelov, K. Ahmad, M. Riegler, N. Ahmad, O. Ostroukhova, P. Halvorsen and N. Conci|
|Title||Deep learning approaches for flood classification and flood aftermath detection|
|Project(s)||Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources|
|Publication Type||Proceedings, refereed|
|Year of Publication||2018|
|Conference Name||Working Notes Proceedings of the MediaEval 2018 Workshop|
|Place Published||Sophia Antipolis, France|
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.