A Building Polygonal Object Alignment Approach for Spatial Data Integration

This paper proposes a building polygon alignment approach for spatial data integration which can be used to improve the spatial data position accuracy. Firstly, this paper uses the minimum bounding rectangle combinatorial optimization algorithm to identify corresponding objects between the integrated data. Then, a pairwise constraint spectrum matching algorithm based on geometric simila-rity is proposed to detect conjugate-point pairs of 1:1, 1:N, and M:N correspondence. The conjugate-point pairs from 1:N, and M:N correspondence inevitably have weak or error corresponding point pair, thus this paper proposes a least-squares algorithm based on the IGG1 weight to align the corresponding objects. The proposed method is applied to align base map data with higher positional accuracy and Google map data with lower positional accuracy. The results show that the proposed method can not only detect conjugate-point pairs of 1:N, and M:N correspondence with complex contours, but also can achieve the accurate alignment between them, and minimize their difference of positional information.