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The challenges that emergency services face when dealing with disasters are becoming increasingly complex. Thus, methods of analysing new digital information for situation assessment and operational planning is of crucial importance. This talk presents an approach for multi-modal analysis of digital data such as geo-social media posts using artificial intelligence (AI), helping to ensure the protection and rescue of people and critical infrastructure. The approach presented aims at the AI-supported and automated analysis of this data so that holistic, spatio-temporal situation information is available to end users. It fuses information from social media including semantic topics, sentiments and emotions, spatial hot spots, and temporal changes. The talk outlines the potential of digital data analysis for operational practice: The research results were tested in a realistic application during a large-scale disaster exercise with around 900 emergency staff. The large-scale exercise, coordinated by an professional operations team, was designed around a once-in-a-century flood event and included four operational phases, namely a building collapse, flooded buildings, the derailment of a dangerous goods train, and people floating in a river. The results of the exercise demonstrate that digital data sources can provide crucial added value for situation assessment and staff work, both in terms of rapid situation assessment and efficient resource and operational planning.
Lecture by Resch Bernd.
