用图神经网络统一解决建筑轮廓简化与聚合，首次探索了图学习在地图综合中的应用。
Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
- 将简化视为节点位置预测，聚合视为边预测，构建统一图学习框架
- GraphSAGE在边预测中表现最优，但精确节点移动仍存挑战
- 聚合比简化更复杂，当前模型难捕捉高层空间关系
地图综合仍是制图学中的基础任务，尤其针对复杂建筑轮廓的简化与聚合。本研究首次探索将基于图的深度学习应用于这两项任务，将简化重构为节点位置预测，聚合重构为图中的边预测，建立统一的图学习框架。我们在多尺度建筑数据集上评估了代表性图神经网络架构（GCN、GAT 和 GraphSAGE），结果显示，GraphSAGE 在边预测精度上表现相对更优，但在精确的节点移动预测方面仍存在持续挑战。除定量性能外，结果表明聚合任务比简化更具复杂性与挑战性，凸显当前深度学习方法在捕捉地图综合中高层次空间关系方面的困难。尽管存在数据不平衡及后处理需求等局限，该研究为推进基于深度学习的自动化地图综合提供了重要启示与方法方向。
原文摘要 · Abstract (English)
Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints. This study presents the first exploratory application of graph-based deep learning to both tasks, reformulating simplification as node movement prediction and aggregation as link prediction within a unified graph learning framework. We evaluate representative graph neural network architectures (GCN, GAT, and GraphSAGE) on multi-scale building datasets, showing that GraphSAGE demonstrates relative strengths in link prediction accuracy, while also revealing persistent challenges in precise node movement prediction. Beyond quantitative performance, the results highlight that aggregation poses greater complexity and challenges than simplification, underscoring the difficulty of capturing higher-level spatial relationships in map generalization with current deep learning approaches. Although limitations such as data imbalance and the need for post-processing remain, the study provides valuable insights and methodological directions for advancing automated map generalization with deep learning approaches.
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