STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data
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Computer Science > Machine Learning
Title:STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data
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.
| Comments: | Accepted at IGARSS 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.10249 [cs.LG] |
| (or arXiv:2608.10249v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10249
arXiv-issued DOI via DataCite (pending registration)
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