Flood Event Image Recognition via Social Media Image and Text Analysis

The emergence of social media has led to a new era of information communication, in which vast amounts of information are available that is potentially valuable for emergency management. This supplements and enhances the data available through government bodies, emergency response agencies, and broadcasters. Techniques developed for visual content analysis can be useful tools to improve current emergency management systems. We present a new flood event scene recognition system based on social media visual content and text analysis. The concept of ontology is introduced that enables the text and image analysis to be linked at an atomic or hierarchical level. We accelerate web image analysis by using a new framework that incorporates a novel “Squiral” (square spiral) Image Processing addressing scheme with the state-of-art “Speeded-up Robust Features”. The focus of recognition was to identify the water or person images from the background images. Image URLs were obtained based on text analysis using English and German languages. We demonstrate the efficiency of the new image features and accuracy of recognition of flood water and persons within images, and hence the potential to enhance emergency management systems. The system for the atomic level recognition was evaluated using flood event related image data available from the US Federal Emergency Management Agency media library and public German Facebook pages and groups related to flood and flood aid. This evaluation was performed for and on behalf of an EU-FP7 Project Security Systems for Language and Image Analysis (Slandail), a system for managing disasters specifically with the help of digital media including social and legacy media. The system is intended to be incorporated by the project technology partners CID GmBH and DataPiano SA.