Linear building pattern recognition via spatial knowledge graph
This work addresses the need for more efficient building pattern recognition in urban planning, though it is incremental as it builds on existing methods with a new technique.
The paper tackles the problem of recognizing linear building patterns in urban areas by applying a spatial knowledge graph approach, achieving the same precision and recall as existing methods while improving recognition efficiency by 5.98 times on a dataset of 1289 buildings.
Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific subgraph patterns can be efficiently obtained by using relevant reasoning tools. Thus, we try to apply the knowledge graph to recognize linear building patterns. First, we use the property graph to express the spatial relations in proximity, similar and linear arrangement between buildings; secondly, the rules of linear pattern recognition are expressed as the rules of knowledge graph reasoning; finally, the linear building patterns are recognized by using the rule-based reasoning in the built knowledge graph. The experimental results on a dataset containing 1289 buildings show that the method in this paper can achieve the same precision and recall as the existing methods; meanwhile, the recognition efficiency is improved by 5.98 times.