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arXiv AI By Abhishek Potnis, Jacob Arndt

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

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The paper explores how graph neural networks (GNNs) can classify changes in electronic navigational charts (ENCs) as critical or non‑critical for maritime safety. By representing ENC objects as graph nodes and their spatial/semantic relationships as edges, the authors encode both old and new chart data into paired graphs and apply GNN architectures to predict risk levels. Experiments with various GNN models, evaluated on expert‑reviewed ENC updates, show that graph‑based representations enhance classification accuracy, offering a scalable method to support ENC maintenance workflows.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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