用大模型让船舶轨迹数据自动补全并可解释
CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
- 基于大模型和知识图谱，自动补全不完整船位数据
- 支持用户交互式查看补全过程与证据链
- 适合航海分析初学者快速理解船舶动向
船舶自动识别系统（AIS）轨迹数据广泛用于海洋分析，但因数据不完整且复杂，非专业用户难以使用。本文提出CLEAR平台，通过利用大语言模型的推理与生成能力，将原始AIS数据转化为完整、可解释且易于探索的船舶轨迹。该平台构建了基于结构化数据的知识图谱（SD-KG），实现轨迹自动补全与标注。演示中，用户可配置参数自动下载并处理AIS数据，观察轨迹补全与注释过程，对比原始与修复段落，并通过专用图谱查看器交互式检视SD-KG证据，获得对船舶移动行为直观透明的理解。
原文摘要 · Abstract (English)
Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompleteness and complexity of AIS data. We present CLEAR, a knowledge-centric vessel trajectory analysis platform that aims to overcome these barriers. By leveraging the reasoning and generative capabilities of Large Language Models (LLMs), CLEAR transforms raw AIS data into complete, interpretable, and easily explorable vessel trajectories through a Structured Data-derived Knowledge Graph (SD-KG). As part of the demo, participants can configure parameters to automatically download and process AIS data, observe how trajectories are completed and annotated, inspect both raw and imputed segments together with their SD-KG evidence, and interactively explore the SD-KG through a dedicated graph viewer, gaining an intuitive and transparent understanding of vessel movements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。