发表机构
Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电子航海图（ENC）变化分类的人工审核效率低、一致性差的问题，提出结合空间上下文编码器与ENC属性编码器的编码方案，用调优梯度提升树在1308张海图对数据集上取得90%、94%的准确率，提升了海事安全相关的ENC维护效率。
AI 中文摘要
电子航海图（Electronic Navigational Charts, ENCs）是用于海事导航系统的地理空间矢量数据集，代表水深、助航设备、交通方案、危险物等水文与航海信息。水文办公室面临的主要挑战是确定给定的海图变化是否对海事安全构成重大或非重大风险。现有工作流程严重依赖人工审核与验证，该过程劳动强度大，难以应对不断涌入的海图更新量，且会产生不同分析人员之间的不一致性。为解决这一挑战，我们提出一种对ENC变化进行自动分类的方法。我们建立了一个基线编码方案，用于将复杂的矢量数据变化转换为结构化表格格式，供分类模型使用。该编码方案的两个关键组件包括：空间上下文编码器，用于用周围地理特征丰富变化表示；以及ENC属性编码器，用于表示修改对象的细致属性值描述。我们在两个不同的运营数据集上评估了所提出的方法，这些数据集包含1308张海图对，涉及超过10万次单独的海图修改。利用所提出编码方案的调优梯度提升树在两个数据集上分别达到90%和94%的准确率，比在未使用空间上下文和属性嵌入的编码上训练的默认超参数模型提升了5-7%。这些结果证明了将机器学习集成到运营地理空间流程中以改进ENC维护并增强海事安全的可行性。最后，我们的实验证明了简单位置和空间聚合方法的有效性，为评估更复杂的空间表示学习技术在该应用中的效果提供了基础。
英文摘要
Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and navigational information such as depths, navigational aids, traffic schemes, and hazards. A major challenge for hydrographic offices is determining whether a given chart change poses a critical or non-critical risk to maritime safety. Existing workflows rely heavily on manual review and verification, which is labor-intensive, scales poorly with the volume of incoming chart updates, and introduces inter-analyst inconsistencies. To address this challenge, we propose a method for automated classification of ENC changes. We establish a baseline encoding scheme to translate complex vector data changes into a structured tabular format for classification models. The two crucial components of the encoding scheme include a spatial context encoder to enrich the change representations with surrounding geographic features, and an ENC attribute encoder to represent nuanced attribute-value descriptions of the modified objects. We evaluate the proposed approach across two distinct operational datasets, comprising 1,308 chart pairs containing over 100,000 individual chart modifications. Tuned gradient-boosted trees leveraging the proposed encoding schemes achieve accuracies of 90% and 94% on the two datasets, yielding a 5-7% improvement over default hyperparameterized models trained on encodings without spatial context and attribute embeddings. These results demonstrate the viability of integrating machine learning into operational geospatial pipelines to improve ENC maintenance and enhance maritime safety. Finally, our experiments demonstrate the effectiveness of simple location and spatial aggregation methods, providing a foundation for evaluating more sophisticated spatial representation learning techniques for this application.
CommentsAccepted at The 34th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '26), November 03--06, 2026, Riverside, CA, USA
