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
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2018
Conference NameWorking Notes Proceedings of the MediaEval 2018 Workshop
Volume2283
PublisherCEUR-WS.org
Place PublishedSophia 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.

Citation Key26259