AuthorsN. Said, K. Pogorelov, K. Ahmad, M. Riegler, N. Ahmad, O. Ostroukhova, P. Halvorsen and N. Conci
TitleDeep learning approaches for flood classification and flood aftermath detection
AfilliationCommunication Systems
Project(s)Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources
Publication TypeProceedings, refereed
Year of Publication2018
Conference NameWorking Notes Proceedings of the MediaEval 2018 Workshop
Place PublishedSophia 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.

Citation Key26259

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