Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MC.2016.134 | Computer |
Keywords | Field | DocType |
Earthquakes,Knowledge based systems,Semantics,Data mining,Geology,Emergency services,Data acquisition | Computer science,Visualization,Computer security,Situation awareness,Emergency management,Knowledge-based systems,Situational ethics,Impromptu,Classifier (linguistics),Semantics | Journal |
Volume | Issue | ISSN |
49 | 5 | 0018-9162 |
Citations | PageRank | References |
10 | 0.47 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Avvenuti | 1 | 267 | 24.14 |
| Cresci, S. | 2 | 235 | 21.79 |
| Fabio Del Vigna | 3 | 23 | 2.44 |
| Maurizio Tesconi | 4 | 281 | 32.06 |