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The extensive use of social media platforms, especially during disasters,creates unique opportunities for humanitarian organizations to gain situationalawareness and launch relief operations accordingly. In addition to the textualcontent, people post overwhelming amounts of imagery data on social networkswithin minutes of a disaster hit. Studies point to the importance of thisonline imagery content for emergency response. Despite recent advances in thecomputer vision field, automatic processing of the crisis-related social mediaimagery data remains a challenging task. It is because a majority of whichconsists of redundant and irrelevant content. In this paper, we present animage processing pipeline that comprises de-duplication and relevancy filteringmechanisms to collect and filter social media image content in real-time duringa crisis event. Results obtained from extensive experiments on real-worldcrisis datasets demonstrate the significance of the proposed pipeline foroptimal utilization of both human and machine computing resources.

Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises