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.

Keywords: data analysis, visualization, data mining, crisis mapping, computing and social issues, emergency response, disaster management, situational awareness

File: http://ieeexplore.ieee.org/document/7469993/