Abnormal Vessel Activity Detection Using a Two-Level Grid Representation of AIS Data

Abstract

The Automatic Identification System (AIS) generates massive volumes of real-time vessel data across vast maritime regions, making manual anomaly detection impractical for Vessel Traffic Controllers. Existing methods struggle with scalability across diverse maritime traffic and often fail to generalize to unseen vessels in dynamic environments. This paper proposes a novel two-level grid-based data representation for AIS, incorporating: 1) a discretization-based location encoder that maps vessel positions to spatial cells, 2) navigational feature weighting combining speed and course with learned importance, and 3) hierarchical anomaly localization using coarse-grained detection and fine-grained pinpointing. Our feature engineering approach attains a precision of 0.91, a recall of 0.92, an F1-score of 0.90, and an accuracy of 0.92, being tested using an unsupervised Isolation Forest model. Evaluated on the Hawaii and Bornholm Island AIS datasets, our method achieves an Area Under the Curve (AUC) of 0.75 for anomaly localization. These results demonstrate the effectiveness of our grid-based representation and feature engineering for AIS anomaly detection.