Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may require decomposing shapes into parts or grouping them into clusters. Existing methods may not recognize all visually aware patterns, and the proximity graph model can be inefficient. To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns. First, we use a property graph to represent the relationships between buildings within and across different scales involved in C-shaped building pattern recognition. Next, we store this knowledge graph in a graph database and convert the rules for C-shaped pattern recognition and enrichment into query conditions. Finally, we recognize and enrich C-shaped building patterns using rule-based reasoning in the built knowledge graph. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from the Gaode Map. Our results show that our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing approaches. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.

本研究提出了一种基于知识图谱的C形建筑模式识别方法，通过属性图，图数据库和基于规则的推理，实现了建筑模式的有效识别。研究结果表明，相较于现有方法，该方法在多级精度上能够取得更高的召回率和更高的识别效率。

Inferring High-level Geographical Concepts via Knowledge Graph and
  Multi-scale Data Integration: A Case Study of C-shaped Building Pattern
  Recognition

通过知识图谱和多尺度数据集成推断高级地理概念：以C形建筑模式识别为例研究