Data science for Emergency Management

Event Organisers: 

Elena Baralis - Politecnico di Torino, Italy
Paolo Garza - Politecnico di Torino, Italy
Laura Irina Rusu - IBM Research Australia
Gandhi Sivakumar - Watson CoC, Master Inventor, IBM Australia

Co-organisers: 

The event is support by I-REACT.

Venue Country: 

  • United States of America

Venue City: 

Boston

Venue: 

The Westin Copley Place, Boston in Boston, MA,USA.

Date: 

11/12/2017 to 14/12/2017

Registration Deadline: 

Friday, September 29, 2017

Training type: 

academic

Language of event: 

English

Target Audience: 

Researchers, Practitioners, Environmental and Governmental bodies

Description: 

Society is increasingly exposed and vulnerable to frequent reoccurrence of natural disasters exacerbated by climate change. In the event of a disaster, there is huge amount of heterogenous data generated by the people and automated systems. For instance, social network data generated by citizens and first responders, satellite images of the affected areas, flood maps generated by drones. This has become a huge global issue that needs to be addressed. To convert this massive heterogenous crisis data into valuable knowledge, there is need to integrate it and extract knowledge in near-real time by means of novel data analytics solutions. Although, currently the analysis is focused on one single type of data (e.g social media, or satellite images).Their integration into big data analytics systems capable of building accurate predictive now cast and forecast models will provide effective support for emergency management.

The workshop aims at involving researchers, practitioners and environmental and governmental bodies to foster discussion on emergency management analytics open issues and provide interesting insights for future actions in the natural hazard management area.

Some of the topics that could be covered include but are not limited to:

  • Big Data Analytics for emergency management

  • Decision Support Systems for emergency management using big Spatio-temporal data

  • Artificial Intelligence (Cognitive Computing) and Emergency management

  • Crowdsourcing, gamification, and social media data for natural hazard prevention and management

  • Hazard Nowcast and Forecast models based on the integration of earth observations, social media, and crowdsourcing

  • Climate Change Models, Risk Maps for supporting emergency management

  • Real-time Spatial Crisis Data acquisition and processing (satellite images, flood maps, etc.)

  • Crisis Data collection by means of UAVs

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