# Flood Detection via Twitter Streams using Textual and Visual Features

Source: [Flood Detection via Twitter Streams using Textual and Visual Features](<https://www.sota2.com/research/paper/68fa68d21e209131cea1e5c9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:45:52.748Z

Authors: Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci, Ala AL-Fuqaha

Published: 2020-11-30T16:09:11.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.14944>)

[PDF](<https://arxiv.org/pdf/2011.14944.pdf>)