Context-Enriched Natural Language Descriptions of Vessel Trajectories

TL;DR

This paper introduces a framework that transforms raw vessel AIS data into semantically rich, human-readable descriptions by segmenting and enriching trajectories with contextual information, supporting advanced maritime reasoning.

Contribution

The authors propose a novel context-aware abstraction method that enhances vessel trajectory data with multi-source information for improved interpretability and reasoning.

Findings

Generated natural language descriptions are of high quality across multiple LLMs.

Semantic density of vessel data is significantly increased.

The approach facilitates downstream maritime analytics and reasoning.

Abstract

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.