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This paper proposes CRHT, a Continuous Regression Hybrid Transformer for vessel trajectory prediction using AIS data, featuring an online K-means cluster sampling strategy and a CNN-Transformer hybrid architecture to address geographic bias and improve short-term forecasting accuracy.
Presents STCAD, a scalable framework using BERT-based encoding and CURE clustering to perform trajectory clustering and anomaly detection on terabyte-scale AIS maritime data, demonstrating stable clusters and clear separation of anomalous vessel behavior.
A data journalism piece analyzes 50,000 boat names from NOAA vessel traffic data, highlighting puns, jokes, and pop culture references, with an interactive searchable visualization.
MoCo-AIS is a unified contrastive learning framework for computing similarity of vessel trajectories, evaluated on large-scale AIS datasets.
This paper introduces M-CTX, an exact and scalable spatial context retrieval framework for trajectory analytics that reduces context construction time from 17 CPU-days to 1.8 hours on a 5.48M-anchor maritime corpus, by replacing brute-force stages with index-backed operators.
EnvShip-Bench is a new benchmark for short-term vessel trajectory prediction, built from large-scale AIS data with standardized protocols and environmental context extensions.