Data Science for Emergency Management(DSEM 2017)
December 11-14,2017 ,Boston,MA,USA

Society as a whole is increasingly exposed and vulnerable to natural disasters because extreme weather events, exacerbated by climate change, are becoming more frequent and longer. To address this global issue, novel advanced data analytics solutions able to cope with heterogeneous big data sources are needed. The huge amount of data generated by people and automatic systems during natural hazard events (e.g., social network data generated by citizens and first responders, satellite images of the affected areas, flood maps generated by drones), is typically referred to as Big Crisis Data. To transform this overload of heterogeneous information into valuable knowledge, we need to integrate it and extract knowledge in near-real time by means of novel data analytics solutions. Currently, the analysis is focused on one single type of data (e.g., social media data, or satellite images). Their integration into big data analytics systems capable of building accurate predictive nowcast and forecast models will provide effective support for emergency management.