Abstract: 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.
Lecturer Short CV: Bernd Resch is an Associate Professor at University of Salzburg’s Department of Geoinformatics – Z_GIS and a Visiting Scholar at Harvard University (USA). He heads the Geo-social Analytics Lab and the iDEAS:lab. Bernd Resch did his PhD in the area of “Live Geography” (real-time monitoring of environmental geo-processes) together with University of Salzburg and MIT. His research interest revolves around understanding cities as complex systems through analysing a variety of digital data sources, focusing on developing geospatial machine learning algorithms to analyse human-generated data like social media posts and physiological measurements from wearable sensors. The findings are relevant to a number of fields including urban research, disaster management, epidemiology, and others. Bernd received the Theodor Körner Award for his work on “Urban Emotions”. Amongst a variety of other functions, he is an Editorial Board Member of IJHG, IJGI and PLOS ONE, a scientific committee member of various international conferences (having chaired several conferences), speaker of the Faculty of Digital and Analytical Sciences at PLUS, and an Executive Board member of Spatial Services GmbH.