mlsanityData to 7 Oct 2026

Context-Enriched Natural Language Descriptions of Vessel Trajectories

arXiv 2603.12287 ↗First posted 8 Mar 2026cs.AI cs.CL cs.DBcited by 0

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Abstract (arXiv)

We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpretable by humans and directly usable by machine reasoning systems. We propose a context-aware trajectory abstraction framework that segments noisy AIS sequences into distinct trips each consisting of clean, mobility-annotated episodes. Each episode is further enriched with multi-source contextual information, such as nearby geographic entities, offshore navigation features, and weather conditions. Crucially, such representations can support generation of controlled natural language descriptions using LLMs. We empirically examine the quality of such descriptions generated using several LLMs over AIS data along with open contextual features. By increasing semantic density and reducing spatiotemporal complexity, this abstraction can facilitate downstream analytics and enable integration with LLMs for higher-level maritime reasoning tasks.

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References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
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  6. 2023Large Language Models for Spatial Trajectory Patterns Miningcited by 27
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  12. 2025Using LLMs for Analyzing AIS Datacited by 1
1 more
  1. 2026Data-Driven Trajectory Imputation for Vessel Mobility Analysiscited by 1

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  1. 2026M3-Former: Multimodal Transformer with Mixture-of-Experts for Long-Term Vessel Trajectory Prediction
  2. 2025VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language
  3. 2026NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework
  4. 2026Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios
  5. 2026Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
  6. 2026ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization
  7. 2025TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
  8. 2025Understanding the Geospatial Reasoning Capabilities of LLMs: A Trajectory Recovery Perspective