• Corpus ID: 41845203

Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks

@inproceedings{Bischke2017DetectionOF,
  title={Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks},
  author={Benjamin Bischke and Prakriti Bhardwaj and Aman Gautam and Patrick Helber and Damian Borth and Andreas R. Dengel},
  booktitle={MediaEval},
  year={2017}
}
This paper presents the solution of the DFKI-team for the Multimedia Satellite Task at MediaEval 2017. [] Key Method Additionally, we extend existing network architectures for semantic segmentation to incorporate RGB and Infrared (IR) channels into the model. Our results show that IR information is of vital importance for the detection of flooded areas in satellite imagery.
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TLDR
This work aims to detect floods and also give relevant information about the flood situation such as the water level and the extension of the flooded regions, as specified in the three subtasks, for which of them a specific solution is proposed.
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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…
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