A Method for Recognizing I-Shaped Building Patterns Utilizing Multi-Scale Data and Knowledge Graph

Building pattern recognition is essential for understanding the dynamics of urban development, facilitating automatic map synthesis, and aiding in municipal planning Sefforts. Traditional research methods, which rely solely on geometric feature extraction from isolated objects, struggle to capture the complex and visually significant building patterns within urban environments, often suffering from low accuracy and robustness. This paper proposes a novel approach for recognizing I-shaped building patterns utilizing multi-scale data and knowledge graphs. The process begins by extracting inter-building relationships at and across different scales based on geometric and spatial rules derived from the Smallest Bounding Rectangle (SBR) representation, thereby establishing a comprehensive framework for recognizing I-shaped building patterns. This framework is encoded into a knowledge graph that translates specific scale-based and cross-scale recognition rules into conditions for knowledge graph reasoning. Utilizing rule-based reasoning within this framework, our method effectively identifies I-shaped building patterns that align with human visual principles. Experimental results underscore the efficacy of this approach, with significant enhancements in the recognition of I-shaped patterns being noted. Specifically, when compared to traditional methods that overlook multi-scale data and visual dynamics, our approach achieved a 24% increase in recall rate in Lanzhou and a 52.75% increase in London, while also Amaintaining high precision.