评估用于海图变更重要性分类的图神经网络
Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts
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中文总结 AI 辅助
本研究针对ENC对象变更的航行安全重要性分类问题，提出将ENC表示为图对的方法，通过评估多种GNN架构，实现了更优的分类效果，为ENC维护提供了可扩展的自动化方案。
中文摘要 AI 辅助
图神经网络（GNNs）是一类适用于图结构数据学习的神经网络，其在空间数据上的应用是自然延伸，但目前尚不明确哪种消息传递操作、架构配置及图表示最适合对电子航海图（ENCs，用于航海的地理空间矢量数据集）中对象的变更进行分类。维护这些数据集是一项挑战，根据ENCs中对象变更对航行安全的重要性进行分类尤为关键。本文提出将这些矢量航海数据集表示为图结构，其中空间对象作为节点，其空间和语义关系构成边；将新旧两个ENC数据集分别编码为一对图，将该任务构建为图对分类问题。基于该表示，本文研究使用GNN架构对编码后的图是否构成航行安全的关键或非关键风险进行分类，在航海专家审核过的ENC变更上训练并评估了多种GNN架构及模型配置。结果表明，基于图的表示可改进ENC更新的分类，为自动化或优化ENC维护工作流提供了可扩展的方法。
英文摘要
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.
发表机构
- Oak Ridge National Laboratory（橡树岭国家实验室）
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