Enhancing building pattern recognition through multi-scale data and knowledge graph: a case study of C-shaped patterns

Building pattern recognition is important for understanding urban forms, automating map generalization, and visualizing 3D city models. However, current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human vision. Moreover, these approaches also suffer from inefficiencies when applying proximity graph models. To address these limitations, we propose a framework that leverages multi-scale data and a knowledge graph, focusing on recognizing C-shaped building patterns. We first employ a specialized knowledge graph to represent the relationships between buildings within and across various scales. Subsequently, we convert the rules for C-shaped pattern recognition and enhancement into query conditions, where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other scales. Finally, rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building patterns. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from AMap, and our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing methods with similar precision rates. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.