CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
Hengyu Liu, Tianyi Li, Haoyu Wang, Kristian Torp, Yushuai Li, Tiancheng Zhang, Torben Bach Pedersen, Christian S. Jensen
TL;DR
AIS trajectory data suffer from incompleteness and semantic complexity, hindering analysis for non-experts. CLEAR introduces a knowledge-centric platform that uses a Structured Data-derived Knowledge Graph (SD-KG) and Large Language Models (LLMs) to distill maritime knowledge from AIS and guide trajectory imputation with evidence-backed explanations. It operates via a data–knowledge–data loop comprising SD-KG construction and knowledge-driven trajectory imputation, overseen by a workflow manager for scalability. Key contributions include the SD-KG structure with static attributes, behavior patterns, and imputation methods, an explainable imputation pipeline, and interactive tools for segment-level analysis and SD-KG exploration, enabling auditable, transparent maritime analytics.
Abstract
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.