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

Deep learning approaches for flood classification and flood aftermath detection / Said, Naina; Pogorelov, Konstantin; Ahmad, Kashif; Riegler, Michael; Ahmad, Nasir; Ostroukhova, Olga; Halvorsen, Pål; Conci, Nicola. - 2283:50(2018). (Intervento presentato al convegno 2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018 tenutosi a Sophia Antipolis, France nel 29-31 October 2018).

Deep learning approaches for flood classification and flood aftermath detection

Ahmad, Kashif;Conci, Nicola
2018-01-01

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 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
2018
CEUR Workshop Proceedings
GERMANY
CEUR-WS
Said, Naina; Pogorelov, Konstantin; Ahmad, Kashif; Riegler, Michael; Ahmad, Nasir; Ostroukhova, Olga; Halvorsen, Pål; Conci, Nicola
Deep learning approaches for flood classification and flood aftermath detection / Said, Naina; Pogorelov, Konstantin; Ahmad, Kashif; Riegler, Michael; Ahmad, Nasir; Ostroukhova, Olga; Halvorsen, Pål; Conci, Nicola. - 2283:50(2018). (Intervento presentato al convegno 2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018 tenutosi a Sophia Antipolis, France nel 29-31 October 2018).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/225963
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