Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine-tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.

中文翻译：

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整合领域知识和图卷积神经网络支持河网选择

深度学习越来越多地被用来提高地图泛化的智能。利用深度学习的基于矢量的地图泛化是研究的重要途径。然而，存在三个问题：（1）将矢量数据转化为深度学习数据范式；(2)克服样本数量的限制；（3）确定现有知识是否可以加速深度学习。为了解决这些问题，本研究以河网选择为例，提出了一个将水文知识集成到图卷积神经网络（GCNN）中的框架。该框架由以下步骤组成：构建河流网络的对偶图（DG_RN），提取领域知识作为DG_RN的节点属性，开发用于选择的GCNN架构，以及设计微调规则来细化GCNN结果。实验表明，我们的框架优于现有的机器学习和使用不同数据集的传统特征排序方法，并且在选择后实现了良好的形态一致性。此外，这些结果表明DG_RN满足图深度学习的数据范式，并且整合现有特征（即Strahler编码、支流数量、邻近河流之间的距离和上游流域面积）的框架减轻了GCNN对GCNN的依赖。大量样本并增强其性能。