将船舶轨迹转化为可读可计算的语义描述。
Context-Enriched Natural Language Descriptions of Vessel Trajectories
- 分段处理噪声轨迹，标注移动行为并融合地理、气象等上下文。
- 生成的描述语义密度高，时空复杂度降低。
- 适合需要船舶行为理解的海事分析与大模型应用。
针对从船舶自动识别系统（AIS）获取的原始轨迹数据难以被人类理解或机器推理系统直接使用的问题，本文提出一种上下文感知的轨迹抽象框架。该框架将带有噪声的AIS序列分割为多个独立航程，每个航程由清洁且带移动标注的片段组成，并进一步融合周边地理实体、海上导航特征及气象条件等多源上下文信息。关键在于，此类结构化表示可支持利用大语言模型（LLMs）生成可控的自然语言描述。我们在多个LLM上评估了基于AIS数据与开放上下文特征生成描述的质量。通过提升语义密度并降低时空复杂度，该方法有助于下游分析，并推动与大语言模型结合，实现更高层次的海事推理任务。
原文摘要 · Abstract (English)
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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