CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Liu, Hengyu, Li, Tianyi, Wang, Haoyu, Torp, Kristian, Li, Yushuai, Zhang, Tiancheng, Pedersen, Torben Bach, Jensen, Christian S.
Format: Preprint
Publié: 2026
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866918413913292800
author Liu, Hengyu
Li, Tianyi
Wang, Haoyu
Torp, Kristian
Li, Yushuai
Zhang, Tiancheng
Pedersen, Torben Bach
Jensen, Christian S.
author_facet Liu, Hengyu
Li, Tianyi
Wang, Haoyu
Torp, Kristian
Li, Yushuai
Zhang, Tiancheng
Pedersen, Torben Bach
Jensen, Christian S.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_08482
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
Liu, Hengyu
Li, Tianyi
Wang, Haoyu
Torp, Kristian
Li, Yushuai
Zhang, Tiancheng
Pedersen, Torben Bach
Jensen, Christian S.
Databases
Artificial Intelligence
H.2.8
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.
title CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
topic Databases
Artificial Intelligence
H.2.8
url https://arxiv.org/abs/2602.08482