Publication Date:
2016
abstract:
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.
Iris type:
01.01 Articolo in rivista
Keywords:
computing and social issues; crisis mapping; data analysis; data mining; disaster management; emergency response; situational awareness; visualization
List of contributors:
DEL VIGNA, Fabio; Tesconi, Maurizio; Cresci, Stefano
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