Name
Abstract | ||
|---|---|---|
Maritime Situational Awareness (MSA), the capability of understanding events, circumstances and activities within and impacting the maritime environment, can be greatly improved by the automatic identification and classification of vessel activity. Enhancing coverage of existing technologies such as Automatic Identification System (AIS) provides the possibility to integrate and enrich services and information already available in the maritime domain. In this paper, we propose a method that automatically extracts knowledge from ship reporting positioning data. In particular, we apply this architecture to the practical scenario of automatically discovering fishing areas based on historical AIS data broadcast by fishing vessels. |
Year | Venue | Keywords |
|---|---|---|
2014 | Information Fusion | aquaculture,data mining,fishing industry,marine engineering,ais,automatic identification system,automatic knowledge extraction,automatic vessel activity classification,automatic vessel activity identification,fishing footprints mapping,maritime environment,maritime situational awareness,positioning data,sea,vessel activity discovery,trajectory,clustering algorithms,noise |
DocType | Citations | PageRank |
Conference | 7 | 0.59 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabio Mazzarella | 1 | 35 | 3.94 |
| Michele Vespe | 2 | 166 | 14.03 |
| Dimitrios Damalas | 3 | 7 | 0.59 |
| Giacomo Osio | 4 | 10 | 1.01 |