Semi-supervised vessel trajectory analysis for unregulated fishing activity detection

Maritime crimes such as Illegal, Unreported, and Unregulated (IUU) fishing, piracy, and smuggling pose significant threats to marine ecosystems, trade, and coastal security, especially in developing regions. Automatic Identification System (AIS) data offers a scalable solution for vessel monitoring, but the use of fully supervised machine learning models is constrained by the substantial manual effort and expert input required to label training data. To address this challenge, authors propose a semi-supervised machine learning pipeline that classifies vessel activities from AIS data without relying on pre-labeled datasets. Our approach leverages scaled geospatial and temporal features, including latitude, longitude, speed, and time difference, to train multiple Hidden Markov Models (HMMs) on trajectory segments. These segments are then grouped using similarity-based K-means clustering and subsequently classified with supervised models, including Random Forest and Long Short-Term Memory (LSTM) networks. The pipeline effectively identifies and labels maritime activities such as sailing, fishing, idling, and other activities. Experiments were conducted on a dataset comprising 156,379 AIS points, partitioned into training and test sets. The LSTM-based supervised model achieved an F1 score of 0.86 on the local test set, while the end-to-end pipeline achieved an F1 score of 0.5 on a global evaluation set. These results demonstrate the feasibility of automating maritime activity classification through artificial intelligence and hybrid learning, offering a scalable solution for real-world maritime surveillance.