Context-Enriched Natural Language Descriptions of Vessel Trajectories
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 ...
| Authors | Kostas Patroumpas, Alexandros Troupiotis-Kapeliaris, Giannis Spiliopoulos et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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