Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts

Researchers propose using graph neural networks (GNNs) to classify changes in electronic navigational charts (ENCs) as critical or non-critical to maritime safety, representing ENC datasets as graph pairs and testing multiple architectures on expert-reviewed changes. The study, posted on arXiv (2609.02996v1), found that graph-based representations improve classification accuracy, offering a scalable method to automate ENC maintenance workflows.

arXiv:2609.02996v1 Announce Type: new Abstract: Graph neural networks GNNs are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a natural extension, however its relatively unclear which message-passing operations, architectural configurations, and graph representation is best suited for classifying changes to objects in electronic navigational charts ENCs --geospatial vector datasets used for marine navigation. Maintaining these datasets is a challenge, and categorizing changes to objects in the ENC based on their significance to navigational safety is of particular importance. Here, we propose to represent these vector navigation datasets as a graph structure where the spatial objects serve as nodes and their spatial and semantic relationships form edges. We encode both the old ENC dataset and new ENC dataset into a pair of graphs and frame the task as a graph-pair classification problem. Building on this representation, we investigate the use of GNN architectures to classify whether the encoded graphs constitutes a critical or non-critical risk to navigational safety. We train and evaluate several GNN architectures and model configurations on ENC changes reviewed by maritime experts. Our results demonstrate that graph-based representations improve the classification of ENC updates, providing a scalable approach for automating or improving ENC maintenance workflows.