  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.
