GIST: Gear Type Identification by Spatiotemporal Trajectory Transformation for Monitoring Fisheries

Abstract

Illegal, Unreported, and Unregulated (IUU) fishing aggravates the global crisis caused by overfishing, threatening the sustainability of marine ecosystems and fisheries worldwide. Distinctive operational characteristics of fishing vessels result in unique footprints on marine environments and socio-economic structures, depending on their fishing method and gear type such as trawlers with non-selective gear that disrupts the seabed, purse seiners using Fish Aggregating Devices (FADs), and longliners notorious for high bycatch rates. As these vessels play an essential role in commercial fishing and the industry, effective monitoring, regulation, and enforcement are critical to mitigate the devastating consequences of overfishing and promote sustainable fishing practices. To this end, this paper introduces a novel multi-stage method for Gear type Identification by Spatiotemporal trajectory Transformation (GIST). This method proposes a data-centric approach that employs domain knowledge to facilitate the deployment of an efficient and accurate analysis of operational patterns of fishing vessels derived from Automatic Identification System (AIS) data. Our method first extracts fishing patterns from vessel trajectories to refine data integrity and isolate only the most relevant activities, thereby ensuring a more accurate result. Next, it encapsulates the distributional insights of fishing activities into fixed-sized "images" as actionable input for a multi-class CNN-based classifier. Utilizing GIST bypasses complicated linear analyses of time series data for rendering lengthy trajectories, advancing an efficient gear type identification with 97% accuracy. To the best of our knowledge, GIST is the first to use a multi-stage method to distinguish three principal gear types widely used globally. Our experiments confirm GIST's practicability and effectiveness, marking a significant advancement towards stricter enforcement of regulations in the fight against IUU fishing.

Cite

Text

Shahir et al. "GIST: Gear Type Identification by Spatiotemporal Trajectory Transformation for Monitoring Fisheries." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35056

Markdown

[Shahir et al. "GIST: Gear Type Identification by Spatiotemporal Trajectory Transformation for Monitoring Fisheries." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/shahir2025aaai-gist/) doi:10.1609/AAAI.V39I27.35056

BibTeX

@inproceedings{shahir2025aaai-gist,
  title     = {{GIST: Gear Type Identification by Spatiotemporal Trajectory Transformation for Monitoring Fisheries}},
  author    = {Shahir, Amir Yaghoubi and Charalampous, Tilemachos and Keramati, Mahsa and Movafagh, Fatemeh and Glässer, Uwe and Wehn, Hans},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {28349-28358},
  doi       = {10.1609/AAAI.V39I27.35056},
  url       = {https://mlanthology.org/aaai/2025/shahir2025aaai-gist/}
}