An axis-matching approach to combined collinear pattern recognition for urban building groups

Abstract

Building pattern extraction is one of the crucial components in map generalization. Previous studies mainly focus on the homogeneity between individual buildings in the collinear patterns, while pay less attentions to the complex collinear patterns, in which buildings are locally heterogeneous. Therefore, we put forward a novel combined collinear pattern to extend the existing pattern typology. Then, we present an axis-matching pattern recognition approach which transforms pattern extraction into an axis-matching problem, taking the advantages of computational geometry and visual perception theories. First, we use the axes of the smallest bounding rectangle to represent buildings. Second, different candidate sets of matched axis pairs are built considering the project distance, length and orientation similarities. Third, the strategy for recognizing combined collinear patterns is designed according to similarity criteria. The proposed approach was tested using real topographic data. The experiment results show that the proposed approach is reasonable and efficient, and the correctness and completeness of extracted combined collinear patterns have been guaranteed.

Keywords:

• Building patterns
• pattern recognition
• urban buildings
• map generalization

Acknowledgment

The authors would like to thank the editors and the anonymous reviewers for their helpful and constructive comments that greatly contributed to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available at https://www.pdok.nl/downloads/-/article/basisregistratie-topografie-brt-topnl.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China under [grant number 41471386, 41801396].