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 · Iris (University of Trento) · 2018
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, 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.