Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea

Abstract

Overfishing is a critical catastrophe to the ecosystem and the global food chain. The leading causes are Illegal Unreported and Unregulated Fishing (IUU Fishing) linked to illegal labor. EU and the US have set up the fisheries policy that emphasis on traceability. The traceability principle is to monitor the entire seafood supply chain (Sea to Table). FAO’s technology gap analysis reveals that there is a lack of reliable and affordable automated systems or a lack of links to traceability. The challenge of traceability is tracing back to the catch source with existing data and technology. This study aims at the novel concept of a combination of global and local features of trajectory data for fishing vessel behavior identification and enabling seafood transparency. We present a new technique on a local feature of time series and transform the trajectory pattern to global features for Deep Learning. We apply this technique to AIS and VMS data of Thai fishing vessels (Surrounding Nets, Trawl, Longliner, and Reefer). Fishing vessel behaviors were classified as Fishing, Non-fishing, and Transshipment. Our proposed method gives a robust average accuracy result (97.50%). This concept could solve the IUU Fishing and enable traceability at sea, including monitoring, maritime, and marine resources conservation systems.

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Correspondence to Supaporn Kiattisin.

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Chuaysi, B., Kiattisin, S. Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea. Wireless Pers Commun 115, 2971–2993 (2020). https://doi.org/10.1007/s11277-020-07200-w

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Keywords

  • Traceability at sea
  • Fishing vessels behavior
  • Trajectory and time series analysis
  • Global and local features
  • KNN for MLP