Name
Abstract | ||
|---|---|---|
In this paper, an algorithm is proposed to automatically produce hierarchical graph-based representations of maritime shipping lanes extrapolated from historical vessel positioning data. Each shipping lane is generated based on the detection of the vessel behavioural changes and represented in a compact synthetic route composed of the network nodes and route segments. The outcome of the knowledge discovery process is a geographical maritime network that can be used in Maritime Situational Awareness (MSA) applications such as track reconstruction from missing information, situation/destination prediction, and detection of anomalous behaviour. Experimental results are presented, testing the algorithm in a specific scenario of interest, the Dover Strait. |
Year | Venue | Keywords |
|---|---|---|
2014 | Fusion | knowledge discovery,geographic information systems,network nodes,trajectory,security,anomaly detection |
Field | DocType | Citations |
Graph,Computer science,Situation awareness,Node (networking),Knowledge extraction,Artificial intelligence,Machine learning | Conference | 7 |
PageRank | References | Authors |
0.72 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virginia Fernandez Arguedas | 1 | 27 | 4.20 |
| Giuliana Pallotta | 2 | 90 | 6.29 |
| M. Vespe | 3 | 10 | 1.15 |