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.

Tracking fishing vessels individually plays a pivotal role in fisheries monitoring, control, and surveillance. Fishing trip is the most appropriate granularity level to study routine fishing activity patterns. Since self-reported information about fishing vessel trips is notoriously unreliable, we propose here TripTracker, an unsupervised learning approach to partition raw trajectories of ships and boats engaging in fishing into trips and identify trip types. TripTracker first partitions a fishing trip into micro-activities, then uses cluster analysis to confirm the microactivity type. Next, it employs multiple Hidden Markov Models to partition the trip into segments, each of which representing a routine activity. Finally, TripTracker utilizes maritime contextual information to differentiate various fishing trip types, revealing actionable knowledge about vessel activities and their operations. Our experimental evaluation on a large real-world fishing vessel trajectory dataset, confirms TripTracker’s practicability and effectiveness for enhancing maritime domain awareness.

In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets from vessel tracking services. Dark fishing refers to stealthy fishing operations by vessels trying to hide their illicit activities related to various forms of illegal fishing-one of the most serious threats to world fisheries and fish populations worldwide as well as to global food security. Our approach builds on profiling and ranking fishing vessels by analyzing their routine operations over extended time periods to uncover abnormal activity patterns associated with dark fishing. The focus is on vessel movement patterns rendered as a trajectory with defined starting and endpoints such as ports and known anchorage locations. Specifically, we analyze scenarios where the fishing pattern, with the fishing gear in the water, is obscured in a vessel's reported trip data. Our experimental evaluation, using a large dataset of fishing vessel trajectories from coastal waters of North America, shows the effectiveness and efficiency of the proposed method in differentiating between suspicious and normal fishing vessels irrespective of the vessel type.