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.

翻译：有效的建筑模式识别对于理解城市形态、自动化地图综合及三维城市模型可视化至关重要。现有研究多采用基于视觉感知规则和邻近图模型的独立对象方法进行模式提取。然而，由于人类视觉是基于部件的感知系统，模式识别可能需要将形状分解为部件或将其聚合成簇。现有方法可能无法识别所有视觉可感知的模式，且邻近图模型效率较低。为提升效率与有效性，我们利用知识图谱集成多尺度数据，聚焦于C形建筑模式的识别。首先，我们使用属性图表示C形建筑模式识别中涉及的不同尺度内及跨尺度的建筑关系。其次，将该知识图谱存储于图数据库中，并将C形模式识别与增强规则转换为查询条件。最终，通过内置知识图谱的规则推理实现C形建筑模式的识别与增强。我们使用来自高德地图的三级细节（LOD）多尺度数据验证了方法的有效性。结果表明，与现有方法相比，本方法在LOD1、LOD2和LOD3上的召回率分别提升26.4%、20.0%和9.1%，识别效率分别提升0.91倍、1.37倍和9.35倍。