mlsanityData to 7 Oct 2026

Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)

arXiv 2405.09596 ↗First posted 15 May 2024cs.LG cs.AI stat.MEcited by 3

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Abstract (arXiv)

The prediction of ship trajectories is a growing field of study in artificial intelligence. Traditional methods rely on the use of LSTM, GRU networks, and even Transformer architectures for the prediction of spatio-temporal series. This study proposes a viable alternative for predicting these trajectories using only GNSS positions. It considers this spatio-temporal problem as a natural language processing problem. The latitude/longitude coordinates of AIS messages are transformed into cell identifiers using the H3 index. Thanks to the pseudo-octal representation, it becomes easier for language models to learn the spatial hierarchy of the H3 index. The method is compared with a classical Kalman filter, widely used in the maritime domain, and introduces the Fr'echet distance as the main evaluation metric. We show that it is possible to predict ship trajectories quite precisely up to 8 hours ahead with 30 minutes of context, using solely GNSS positions, without relying on any additional information such as speed, course, or external conditions - unlike many traditional methods. We demonstrate that this alternative works well enough to predict trajectories worldwide.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 59 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

32 of 59 references.

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20 more
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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Reviewcited by 6
  2. 2025Llamarine: Open-source Maritime Industry-specific Large Language Modelcited by 4
  3. 2025Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigationcited by 2