Geometry-Informed Maritime Anomaly Detection Using Probabilistic Roadmaps

Abstract

Maritime anomaly detection is essential for navigational safety and for the protection of critical underwater infrastructure. This paper proposes a geometry-informed supervised framework for detecting anomalous vessel trajectories in the Baltic Sea using Automatic Identification System (AIS) data. A Probabilistic Roadmap (PRM) is constructed over the navigable maritime domain and used as a structural prior to project trajectories onto feasible corridors. This representation enables the extraction of interpretable voyage-level features capturing route efficiency, geometric deviation from nominal paths, kinematic variability, and proximity to submarine cables. To address the scarcity of labeled anomalous events, synthetic anomalies are generated through controlled trajectory perturbations and infrastructure-aware distortions, producing a balanced dataset for supervised training. A Random Forest classifier is trained on the resulting feature set and evaluated under cross-validation and a held-out test split. Experimental results show stable generalization performance, achieving a test ROC AUC of 0.837, indicating the effectiveness of embedding navigational feasibility constraints into the anomaly detection process. The proposed approach provides an interpretable and operationally relevant framework for infrastructure-aware maritime monitoring in geometrically complex environments.