Context-Enriched Natural Language Descriptions of Vessel Trajectories
This addresses the problem of maritime data analysis for domain experts by enabling better trajectory interpretation and LLM integration, but it appears incremental as it builds on existing trajectory abstraction and LLM methods.
The paper tackles the problem of transforming raw vessel trajectory data from AIS into structured, semantically enriched representations for human interpretation and machine reasoning. The result is a framework that segments trajectories into clean trips with contextual information, enabling controlled natural language descriptions using LLMs, though no concrete performance numbers are provided.
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.