CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
This addresses the problem of maritime analytics accessibility for non-experts, but it is incremental as it applies existing LLM and knowledge graph methods to a specific domain.
The paper tackles the difficulty of analyzing incomplete and complex AIS vessel trajectory data for non-expert users by presenting CLEAR, a platform that uses LLMs and a knowledge graph to transform raw data into complete, interpretable trajectories, enabling interactive exploration and understanding.
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.