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Effective building pattern recognition is critical for understanding urbanform, automating map generalization, and visualizing 3D city models. Mostexisting studies use object-independent methods based on visual perceptionrules and proximity graph models to extract patterns. However, because humanvision is a part-based system, pattern recognition may require decomposingshapes into parts or grouping them into clusters. Existing methods may notrecognize all visually aware patterns, and the proximity graph model can beinefficient. To improve efficiency and effectiveness, we integrate multi-scaledata using a knowledge graph, focusing on the recognition of C-shaped buildingpatterns. First, we use a property graph to represent the relationships betweenbuildings within and across different scales involved in C-shaped buildingpattern recognition. Next, we store this knowledge graph in a graph databaseand convert the rules for C-shaped pattern recognition and enrichment intoquery conditions. Finally, we recognize and enrich C-shaped building patternsusing rule-based reasoning in the built knowledge graph. We verify theeffectiveness of our method using multi-scale data with three levels of detail(LODs) collected from the Gaode Map. Our results show that our method achievesa higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3compared to existing approaches. We also achieve recognition efficiencyimprovements of 0.91, 1.37, and 9.35 times, respectively.

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