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arXiv · 2011.14944

Flood Detection via Twitter Streams using Textual and Visual Features

Firoj Alam·Zohaib Hassan·Kashif Ahmad·Asma Gul·Michael Reiglar·Nicola Conci·Ala AL-Fuqaha
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Abstract

The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter. In total, we proposed four different solutions including a multi-modal solution combining textual and visual information for the mandatory run, and three single modal image and text-based solutions as optional runs. In the multimodal method, we rely on a supervised multimodal bitransformer model that combines textual and visual features in an early fusion, achieving a micro F1-score of .859 on the development data set. For the text-based flood events detection, we use a transformer network (i.e., pretrained Italian BERT model) achieving an F1-score of .853. For image-based solutions, we employed multiple deep models, pre-trained on both, the ImageNet and places data sets, individually and combined in an early fusion achieving F1-scores of .816 and .805 on the development set, respectively.

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Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci, Ala AL-Fuqaha. 2020-11-30. Flood Detection via Twitter Streams using Textual and Visual Features. https://arxiv.org/abs/2011.14944

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