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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Identifying Fishing Activities from AIS Data with Conditional Random Fields

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DOI: http://dx.doi.org/10.15439/2016F546

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 47–52 ()

Full text

Abstract. Fishing activity detection is important for fishery management to maintain abundant oceans. This paper presents a novel approach to identifying fishing activities from Automatic Identification System (AIS) data using Conditional Random Fields (CRFs). CRFs are popular for solving structured prediction problems such as sequence labeling in natural language processing. To model the conditional probability distributions that can identify fishing activities of the vessel points, we treat attributes of vessel points as observed variables and the fishing and non-fishing labels as hidden variables. We present three experiments and two comparisons to demonstrate the stability and effectiveness of the resulting models.

References

  1. C. Sutton and A. McCallum, “An introduction to conditional random fields,” Machine Learning, vol. 4, no. 4, pp. 267–373, 2011.
  2. J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of the eighteenth international conference on machine learning, ICML, vol. 1, 2001, pp. 282–289.
  3. A. PVS and G. Karthik, “Part-of-speech tagging and chunking using conditional random fields and transformation based learning,” Shallow Parsing for South Asian Languages, vol. 21, 2007.
  4. F. Sha and F. Pereira, “Shallow parsing with conditional random fields,” in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 2003, pp. 134–141.
  5. A. McCallum and W. Li, “Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons,” in Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4. Association for Computational Linguistics, 2003, pp. 188–191.
  6. B. Settles, “Biomedical named entity recognition using conditional random fields and rich feature sets,” in Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications. Association for Computational Linguistics, 2004, pp. 104–107.
  7. L. Liao, D. Fox, and H. Kautz, “Extracting places and activities from gps traces using hierarchical conditional random fields,” The International Journal of Robotics Research, vol. 26, no. 1, pp. 119–134, 2007.
  8. F. Mazzarella, M. Vespe, D. Damalas, and G. Osio, “Discovering vessel activities at sea using ais data: mapping of fishing footprints,” in Information Fusion (FUSION), 2014 17th International Conference on. IEEE, 2014, pp. 1–7.
  9. D. Peel, N. M. Good, and T. Quinn II, “A hidden markov model approach for determining vessel activity from vessel monitoring system data,” Canadian Journal of Fisheries and Aquatic Sciences, vol. 68, no. 7, pp. 1252–1264, 2011.
  10. E. N. de Souza, K. Boerder, S. Matwin, and B. Worm, “Improving fishing pattern detection from satellite ais using data mining and machine learning,” PLOS ONE, vol. 11, no. 7, p. e0158248, 2016.
  11. X. Jiang, D. L. Silver, B. Hu, E. N. de Souza, and S. Matwin, “Fishing activity detection from ais data using autoencoders,” in Canadian Conference on Artificial Intelligence. Springer, 2016, pp. 33–39.
  12. CRF++: Yet Another Toolkit . [Online]. Available: https://taku910.github.io/crfpp/