.png){kind=icon}
.png){kind=icon}
Return
Vessel trajectory classification from a graph network perspective
.png){kind=icon}
OA
AI
DOI：10.1016/j.aei.2025.104082.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Models AIS messages as graphs with spatio-temporal and sequential links. • Classifies vessel status using feature-level attention in a GNN framework. • Combines node and graph-level outputs in a hierarchical multi-level classification. • Achieves 98% accuracy and F1-score, 99% AUC–ROC on UK AIS data. • Supports autonomy, port monitoring, emissions and economic modelling.
Keywords:
Deep learning
Graph Neural Networks
Trajectory classification
Automatic Identification System (AIS)
Ship behaviour
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.9
Papers:
4.0K
Citations:
1.7W
Organization
No organization information available