A comprehensive framework incorporating deep learning for analyzing fishing vessel activity using automatic identification system data

Abstract

Abstract Automatic Identification System (AIS) has emerged as a crucial and cost-effective tool for monitoring ship behavior, widely employed in various fisheries. However, extracting meaningful insights from extensive AIS data to support fishery research remains challenging. In this study, we developed a framework integrating deep learning for marine fishing activity analysis, leveraging AIS data alongside marine environmental factors. Our approach utilized a transformer-based model with a majority vote for classifying fishing vessel types. The model achieved high accuracy, surpassing 90% in vessel type classification using a small subset of AIS records. Our framework employed the Temporal K-Means algorithm to efficiently identify fishing behavior, leveraging the time-series information of AIS data. Subsequently, it mapped fishing hours onto spatial grids to analyze the relationship between fishing activity and environmental factors. Correlation analysis revealed distinct preferences of different vessel types for environmental conditions, influencing their spatial distributions. Trawlers, for instance, exhibited sensitivity to seafloor bottom temperature, whereas seiners were primarily influenced by sea surface density (SSD) and sea surface temperature and gillnetters by SSD. Through this framework, we have established a coherent process to derive valuable insights about fishery resources from AIS data and guide fisheries management.