Back to top

Maritime Situational Awareness

Maritime Situational Awareness

According to the International Maritime Organization IMO, 90% of the global trade is handled by the shipping industry. Maritime monitoring systems support safe shipping by detecting, in real-time, dangerous, suspicious and illegal vessel activities. Such systems typically use the Automatic Identification System AIS, a tracking technology for locating vessels at sea through data exchange. We have been developing a complex event recognition system for maritime monitoring in the Event Calculus that uses AIS as its main source of information, along with contextual geographical information (e.g, Fishing areas, Natura 2000 areas, depth information etc.).

alt text

Recognition methods

We perform complex event recognition using the ‘Event Calculus for Run-Time Reasoning’ (RTEC), an open-source Prolog implementation of the Event Calculus, designed to compute continuous temporal projection queries for pattern matching on data streams.

Detected maritime activities

We detect:

  • anchored vessels,
  • drifting vessels (true heading deviates from the course over ground),
  • tugging operations,
  • piloting,
  • trawling,
  • speed violations,
  • vessel rendezvous,
  • loitering and,
  • search and rescue operations.

See the videos below for some examples (best viewed in full screen mode):


The detected instances of all composite maritime activities mentioned above, over a period of 6 months (October 2015-March 2016), in the area of Brest, France, is available here. The AIS signals of the dataset are available here, while a semantic annotation of the dataset is available here.


  • Fikioris G., Patroumpas K., Artikis A., Paliouras G. and Pitsikalis M., Fine-Tuned Compressed Representations of Vessel Trajectories.
    In International Conference on Information and Knowledge Management (CIKM), 2020. PDF Slides

  • Katzouris N., Artikis A. and Paliouras G., Parallel Online Event Calculus Learning for Complex Event Recognition.
    In Future Generation Computer Systems, 94, pp. 468–478, 2019. PDF BibTeX DOI

  • Michelioudakis E., Artikis A. and Paliouras G., Semi-Supervised Online Structure Learning for Composite Event Recognition.
    In Machine Learning, 108(7), pp. 1085–1110, 2019. PDF BibTeX DOI

  • Pitsikalis M., Artikis A., Dreo R., Ray C., Camossi E., and Jousselme A., Composite Event Recognition for Maritime Monitoring.
    In 13th International Conference on Distributed and Event-Based Systems (DEBS), pp. 163–174, 2019 PDF Slides BibTeX DOI

  • Tsilionis E., Artikis A. and Paliouras G., Incremental Event Calculus for Run-Time Reasoning.
    In 13th International Conference on Distributed and Event-Based Systems (DEBS), pp. 79–90, 2019. PDF Slides BibTeX DOI

  • Santipantakis G., Vlachou A., Doulkeridis C., Artikis A., Kontopoulos I. and Vouros G., A Stream Reasoning System for Maritime Monitoring.
    In 25th International Symposium on Temporal Representation and Reasoning (TIME), pp. 20:1–20:17, 2018. PDF BibTeX DOI

  • Patroumpas K., Alevizos E., Artikis A., Vodas M., Pelekis N., Theodoridis Y., Online event recognition from moving vessel trajectories.
    In GeoInformatica, 21(2), pp. 389–427, 2017. PDF BibTeX DOI

  • Alevizos E., Artikis A. and Paliouras G., Event Forecasting with Pattern Markov Chains.
    In 11th International Conference on Distributed and Event-Based Systems (DEBS), pp. 146–157, 2017. PDF Slides BibTeX DOI

  • Artikis A., Sergot M. and Paliouras G., An Event Calculus for Event Recognition.
    In IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(4), 895–908, 2015. PDF BibTeX DOI