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Cited by 33Maritime Navigation and SafetyMaritime Security and HistoryData Management and Algorithms

The DOI matches its Crossref record.

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

Forti, Nicola, Millefiori, Leonardo M., Braca, Paolo·2019·OCEANS 2019 - Marseille
DOI 10.1109/oceanse.2019.8867429
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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.

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Crossref · 10.1109/oceanse.2019.8867429

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  • Anomaly detection and target tracking can be recast as a special Bayesian state estimation problem for which hybrid Bernoulli filtering can be used.

    “As a result, anomaly detection and target tracking can be recast as a special Bayesian state estimation problem for which recent advances on hybrid Bernoulli filtering [35] can be used.”

    From Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques (2021) · cites this paper as [35]

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Research works printed but not linked: 15

  1. 10.1109/TAES.2018.2808098 · DOI · Paper page
  2. 10.1109/TAES.2016.150596 · DOI · Paper page
  3. 10.1109/BigData.2017.8258101 · DOI
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  5. ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, Interantional Conference on Knowledge Discovery and Data Mining, 4, 226
  6. 10.1093/biomet/41.1-2.100 · DOI · Paper page
  7. 10.1109/TSP.2018.2875887 · DOI · Paper page
  8. 10.1109/ICASSP.2019.8683428 · DOI · Paper page
  9. 10.1016/j.eswa.2012.05.060 · DOI
  10. ristic, 2008, Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction, 11th International Conference on Information Fusion, 1
  11. 10.1109/TITS.2017.2699635 · DOI
  12. 10.1109/ICIF.2006.301661 · DOI
  13. 10.23919/ICIF.2018.8455567 · DOI · Paper page
  14. 2014, International Maritime Organization Safety of Life at Sea (SOLAS) Convention
  15. 10.3390/e15062218 · DOI

Cited by

Sorted by citations

  • Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques

    Soldi, G., Gaglione, D., Forti, N., Millefiori, L. M., Braca, P., Carniel, S., Simone, A. D., Iodice, A., Riccio, D., Daffina, F. C., et al. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2021.3070884 · Cited by 44

    “Further advancements on vector-based knowledge discovery [11], [12] have recently led to the development of an unsupervised graph-based methodology to identify the spatiotemporal dynamics of ship routes, and efficiently extract a compact representation of global maritime patterns from large volumes of historical AIS d…”
    BackgroundMethodInfluentialSelf-citation

Metadata

Authors
Forti, N., Millefiori, L. M., & Braca, P.
Affiliations
NATO Centre for Maritime Research and Experimentation (IT)
ORCID
Venue
OCEANS 2019 - Marseille
Publisher
IEEE
Published
2019-06
Pages
1-5
DOI
10.1109/oceanse.2019.8867429
OpenAlex
W2979995175
Cited by
33
References
0
Citations by year
2025: 2 · 2024: 8 · 2023: 4 · 2022: 9 · 2021: 5 · 2020: 5
FWCI
9.45
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • MTS/IEEE OCEANS Conference (OCEANS) (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Florence Research (University of Florence) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
  • Social Sciences › Political Science and International Relations › Maritime Security and History
  • Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
Keywords
AIS data, Ornstein-Uhlenbeck process, maritime surveillance

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