A Mesh-Based Typification Method for Building Groups with Grid Patterns

Abstract

Building groups with special patterns are common layouts in urban settlement areas, which should be carefully generalized. Typification is considered as an appropriate operator to generalize building groups with grid patterns. As an important operator in building generalization, the purpose of typification is to reduce the number of objects while preserving the original distribution characteristics as much as possible. This study proposes a mesh-based method to typify buildings with grid patterns. Firstly, the pattern is subdivided into perfect grid or grid-like patterns by considering the completeness of the grids. The proposed typification method consists of three steps: (1) generating mesh from the proximity graph of buildings; (2) eliminating triangular meshes; (3) determining the number, positions, and representations of the newly created buildings with the help of the related meshes. The proposed method is modeled as an iterative process to achieve hierarchical typification results, which provides support to the map multiple representation. The experimental results demonstrate that the mesh-based typification method can achieve satisfying results in the perfect grid pattern, as well as the grid-like pattern. The new distribution of the typified buildings preserves the original pattern characteristics.

Pattern Recognition of Complex Distributed Ditches

“…(1) The m-n problem can be solved by detecting collinear relations. It is still a big challenge in map generalization, which was studied for pattern recognition [29] and typification [38]. The model combined several discrete ditch segments as a whole if they meet the rules of collinear relation.…”

“…Finally, the main-tributary relation between the parallel group and rivers should be preserved, and new ditches should be extended to the river lines, as shown in the red dotted box of Figure 19f. Moreover, ditch density differences in different regions and the degree of typification [38] should be studied further.…”

Generative Adversarial Networks to Generalise Urban Areas in Topographic Maps

“…Consequently, most of the approaches for pattern preservation focus on constructing and analysing an adapted proximity structure, and then determine a class for the building group (Christophe and Ruas, 2002;Zhang et al, 2013;Wei et al, 2018;Wang et al, 2020). Then, when the pattern is characterized, typification algorithms can be applied to simplify the pattern while preserving its structure (Regnauld, 2001;Burghardt and Cecconi, 2007;Wang and Burghardt, 2019) . Some other approaches typify buildings without properly defining patterns (Bader et al, 2005;Basaraner and Selcuk, 2008).…”

Building Typification in Map Generalization Using Affinity Propagation Clustering

“…Another representative work was conducted by Wang and Burghard [14], who designed a stroke-based approach to detect and typify linear building groups. They also investigated typification for building groups with grid patterns [15], which appear frequently on urban maps. Their approach first constructed a mesh of the original buildings based on the proximity graph and further determined the positions and shapes of the newly created buildings with the support of the mesh.…”