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.
Avvenuti, M., Cresci, S., Del Vigna, F., & Tesconi, M. (2016). Impromptu Crisis Mapping to Prioritize Emergency Response. Computer, 49(5), 28-37.
Written by STEFANO CRESCI
Published in
Elenco Pubblicazioni - Publications
