STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data

• URL: http://arxiv.org/abs/2608.10249v1
• Date: Mon, 10 Aug 2026 21:32:42 GMT
• Title: STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data
• Abstract summary: We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives.<n> Variable-length trajectories are encoded with a custom BERT-based model trained via masked token modeling and clustered using CURE hierarchical clustering.<n>An intrinsic anomaly detection method based on reconstruction loss and clustering noise assignment identifies irregular navigation patterns.
• Score: 35.18016233072556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives. Variable-length trajectories are encoded with a custom BERT-based model trained via masked token modeling and clustered using CURE hierarchical clustering, producing physically interpretable trajectory groups without requiring a predefined number of clusters. An intrinsic unsupervised anomaly detection method based on reconstruction loss and clustering noise assignment identifies irregular navigation patterns. The framework is demonstrated on a national-scale AIS dataset comprising billions of messages spanning one year, yielding stable trajectory clusters and a clear separation between nominal and anomalous vessel behavior.

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