Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts
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Computer Science > Machine Learning
arXiv:2609.02996 (cs)
[Submitted on 2 Sep 2026]
Title:Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts
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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.
| Comments: | Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2609.02996 [cs.LG] |
| (or arXiv:2609.02996v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02996
arXiv-issued DOI via DataCite (pending registration)
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