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Optimizing vessel trajectory compression for maritime situational awareness

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Abstract

We present an open-source system that can optimize compressed trajectory representations for large fleets of vessels. We take into account the type of each vessel in order to choose a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. We employ a genetic algorithm that converges to a fine-tuned configuration per vessel type without any hyper-parameter tuning. These configurations can provide synopses that retain less than 10% of the original points with less than 20m approximation error in a real world dataset; in another dataset with 90% less samples than the previous one, the synopses retain 20% of the points and achieve less than 80m error. Additionally the level of compression can be chosen by the user, by setting the desired approximation error. Our system also supports incremental optimization by training in data batches, and therefore continuously improves performance. Furthermore, we employ a composite event recognition engine to efficiently detect complex maritime activities, such as ship-to-ship transfer and loitering; thanks to the synopses generated by the genetic algorithm instead of the raw trajectories, we make the recognition process faster while also maintaining the same level of recognition accuracy. Our extensive empirical study demonstrates the effectiveness of our system over large, real-world datasets.

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Data availability

The BR dataset analyzed during the current study is available in [36], https://doi.org/10.5281/zenodo.1167595. The MS dataset was given to us by MarineTraffic, our partner in the INFORE project, and restrictions apply to the availability of these data, and so are not publicly available. Finally, the sources for data used for composite maritime event recognition are summarized in Table 5.

Notes

  1. https://www.marinetraffic.com/

  2. https://github.com/DataStories-UniPi/Trajectory-Synopses-Generator

  3. https://github.com/aartikis/RTEC

  4. https://github.com/GiannisFikioris/Genetic-Algorithm-for-Synopses-Generator

  5. https://deap.readthedocs.io

  6. https://flink.apache.org/

  7. https://en.wikipedia.org/wiki/YAP_(Prolog)

  8. https://www.swi-prolog.org/

  9. https://ec.europa.eu/environment/nature/natura2000/index_en.htm

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Funding

This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070 and from the NSF grant number CCF-1563714. We would also like to thank MarineTraffic for providing the MS dataset..

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Authors and Affiliations

  1. Department of Computer Science, Cornell University, Ithaca, USA

    Giannis Fikioris

  2. Information Management Systems Institute, Athena Research Center, Marousi, Greece

    Kostas Patroumpas

  3. Department of Maritime Studies, University of Piraeus, Piraeus, Greece

    Alexander Artikis

  4. Inst. of Informatics & Telecommunications, NCSR Demokritos, Athens, Greece

    Alexander Artikis & Georgios Paliouras

  5. Department of Computer Science, University of Liverpool, Liverpool, UK

    Manolis Pitsikalis

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  1. Giannis Fikioris
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  2. Kostas Patroumpas
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  5. Georgios Paliouras
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Correspondence to Giannis Fikioris.

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Fikioris, G., Patroumpas, K., Artikis, A. et al. Optimizing vessel trajectory compression for maritime situational awareness. Geoinformatica 27, 565–591 (2023). https://doi.org/10.1007/s10707-022-00475-0

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