
doi: 10.1109/mc.2016.134
handle: 20.500.14243/355262 , 11568/779885
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.
situational awareness, crisis mapping, emergency response, data analysis, disaster management, data mining, computing and social issues; crisis mapping; data analysis; data mining; disaster management; emergency response; situational awareness; visualization, visualization, computing and social issues
situational awareness, crisis mapping, emergency response, data analysis, disaster management, data mining, computing and social issues; crisis mapping; data analysis; data mining; disaster management; emergency response; situational awareness; visualization, visualization, computing and social issues
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