Deep Learning Approaches for Flood Classification and Flood Aftermath Detection

Naina Said, Konstantin Pogorelov, Kashif Ahmad, Michael Riegler, Nasir Ahmad, Olga Ostroukhova, Pål Halvorsen, Nicola Conci
2018 MediaEval Benchmarking Initiative for Multimedia Evaluation  
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 60.59%, 63.58% and 65.03%, respectively. For the second challenge,
more » ... 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.
dblp:conf/mediaeval/SaidPARAOHC18 fatcat:76yg4psqszgm3cxu5wintcrtvu