Chapter

Fusion Methodologies in Crisis Management

pp 177-211

Towards a Crowd-Sensing Enhanced Situation Awareness System for Crisis Management

  • Andrea SalfingerAffiliated withDepartment of Cooperative Information Systems, Johannes Kepler University Linz Email author 
  • , Werner RetschitzeggerAffiliated withDepartment of Cooperative Information Systems, Johannes Kepler University Linz
  • , Wieland SchwingerAffiliated withDepartment of Cooperative Information Systems, Johannes Kepler University Linz
  • , Birgit PröllAffiliated withInstitute for Application Oriented Knowledge Processing, Johannes Kepler University Linz

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Abstract

Natural and man-made crises pose severe challenges on emergency responders, as they need to gain timely Situation Awareness (SAW) in order to decide upon adequate rescue actions. Computational SAW systems aim at supporting humans in rapidly achieving SAW by means of Information Fusion (IF), thus reduce information overload by fusing data stemming from various sensors to situation-level information. Recently, the increasing popularity of social media on mobile devices has enabled humans to act as crowd sensors, who broadcast their observations on the unfolding crisis situation over social media channels. Consequently, SAW systems for crisis management would benefit from exploiting social media as additional data source. Therefore, the aim of this chapter is to investigate upon how crowd-sensing can be incorporated into SAW systems for crisis management, by elaborating on the following issues: How can the SAW system seek and retrieve additional information from social media that may complement the situational picture obtained with other types of sensors? How can the SAW system adapt this crowd-sensing alongside the monitored situation, to keep pace with the underlying real-world incidents? We attempt at illustrating potential solutions towards these questions, by examining how crowd-sensing can deliver input data for SAW systems, elaborating on the challenges such systems need to overcome in order to identify and extract relevant information from social media, and finally, discussing the architecture of a situation-adaptive SAW system capable of exploiting both conventionally sensed data and unstructured social media content.