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 c...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 precisionrates.We,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.展开更多
基金supported by The National Natural Science Foundation of China(No.41871378)The Youth Inno-vation Promotion Association Foundation of Chinese Academic of Sciences(No.Y9C0060)+1 种基金Fundamental Research Funds for the Central Universities(No.070323006)State Key Laboratory of Networking and Switching Tech-nology(No.600123442).
文摘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 precisionrates.We,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.
