Abstract
ABSTRACTBuilding patterns exhibited collectively by a group of buildings are fundamental to understanding urban forms, classifying urban scenes, analyzing urban landscapes, and generalizing maps. The existing studies have used geometric homogeneity or regularity to represent and discover limited patterns for map generalization, or used interval and rectangle algebra to represent relations between spatial objects. These approaches, however, cannot illustrate how patterns are produced by using syntax or grammar (i.e. relations between buildings) to link words (i.e. buildings) into sentences (i.e. building patterns), making it impossible to represent and discover building patterns with diverse structures. This study presents a relation-based approach to formalize and discover arbitrary building patterns at three abstract levels. At the bottom level, a relative and local frame of reference is defined, and 169 basic relations are derived to represent relative positions between buildings. At the middle level, the 169 relations, qualitative angle description, and qualitative size are combined to formalize important semantic relations between two buildings, which include collinear, perpendicular, and parallel relations. At the top level, the relations at the bottom and middle levels are used to formalize three types of building patterns, including collinear patterns, the structured patterns with acceptable names, and other patterns of interest. Algorithms implementing the three levels of relations are presented and applied to demonstrate the effectiveness of the proposed approach in discovering building patterns from databases and querying building patterns. The results indicate that the relational approach is generic to effectively represent and discover building patterns with arbitrary structures. In addition, it complements the existing geometric methods for recognizing building patterns, and the interval and rectangle algebra for representing building relations.
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More From: International Journal of Geographical Information Science
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