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Impromptu crisis mapping to prioritize emergency response

To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy.


Computer, 2016

Autori esterni: Marco Avvenuti (Università di Pisa)
Autori IIT:

Fabio Del Vigna

Foto di Fabio Del Vigna

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: Avvenuti, 2016, Impromptu crisis mapping to prioritize emergency response.pdf

Attività: Social Media Analysis