Towards to Integrate a Multilayer Machine Learning Data Fusion Approach into Crisis Classification and Risk Assessment of Extreme Natural Events

Author:

Antzoulatos Gerasimos,Koulalis Ilias,Karakostas Anastasios,Vrochidis Stefanos,Kompatsiaris Ioannis

Publisher

Springer International Publishing

Reference54 articles.

1. Ahmad, K., Pogorelov, K., Riegler, Μ., Conci, N., & Halvorsen, P. (2017). CNN and GAN based satellite and social media data fusion for disaster detection. In MediaEval Workshop on Working Notes Proceedings.

2. Ahmad, S., Ahmad, K., Ahmad, N., & Conci, N. (2017). Convolutional neural networks for disaster images retrieval. In CEUR Workshop Proceedings.

3. Antzoulatos, G., Giannakeris, P., Koulalis, I., Karakostas, A., Vrochidis, S., & Kompatsiaris, I. (2020). A multi-layer fusion approach for real-time fire severity assessment based on multimedia incidents. In A. L. Hughes, F. McNeill, & C. Zobel (Eds.), 17th international conference on Information Systems for Crisis Response and Management (ISCRAM’20) on proceedings (pp. 75–89). Blacksburg, VA.

4. Antzoulatos, G., Karakostas, A., Vrochidis, S., & Kompatsiaris, I. (2019). The crisis classification component to strengthen the early warning, risk assessment and decision support in extreme climate events. In 4th International Conference on Dynamic of Disasters – DOD 2019 Proceedings, Kalamata.

5. Atrey, P., Anwar, H., El Saddik, A., & Kankanhalli, S. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Systems, 16(6), 345–379.

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