This paper presents an unsupervised approach to extract maritime Patterns of Life (PoL) from historical Automatic Identification System (AIS) data based on a low-dimensional synthetic representation of ship routes. Recent advances in long-term vessel motion modeling through Ornstein-Uhlenbeck mean-reverting stochastic processes allow to encode knowledge about maritime traffic via a compact graph-based model where waypoints are graph vertices and the connections between them, i.e., the navigational legs, are graph edges. The resulting directed graph ultimately leads to the detection and statistical characterization of recurrent maritime traffic patterns. To demonstrate its effectiveness and applicability to real-world case studies, the proposed methodology has been tested on two extensive AIS datasets, collected in the areas of two operational trials of EU-H2020's MARISA (Maritime Integrated Surveillance Awareness) project.

Unsupervised extraction of maritime patterns of life from Automatic Identification System data / Forti N.; Millefiori L.M.; Braca P.. - ELETTRONICO. - 2019-June:(2019), pp. 1-5. (Intervento presentato al convegno OCEANS Marseille 2019) [10.1109/OCEANSE.2019.8867429].

Unsupervised extraction of maritime patterns of life from Automatic Identification System data

Forti N.;
2019

Abstract

This paper presents an unsupervised approach to extract maritime Patterns of Life (PoL) from historical Automatic Identification System (AIS) data based on a low-dimensional synthetic representation of ship routes. Recent advances in long-term vessel motion modeling through Ornstein-Uhlenbeck mean-reverting stochastic processes allow to encode knowledge about maritime traffic via a compact graph-based model where waypoints are graph vertices and the connections between them, i.e., the navigational legs, are graph edges. The resulting directed graph ultimately leads to the detection and statistical characterization of recurrent maritime traffic patterns. To demonstrate its effectiveness and applicability to real-world case studies, the proposed methodology has been tested on two extensive AIS datasets, collected in the areas of two operational trials of EU-H2020's MARISA (Maritime Integrated Surveillance Awareness) project.
2019
Proceedings OCEANS 2019
OCEANS Marseille 2019
Goal 9: Industry, Innovation, and Infrastructure
Forti N.; Millefiori L.M.; Braca P.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1313551
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