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

Xiao Wang,Dirk Burghardt

doi:10.3390/ijgi8040168

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.

Highlights

Map generalization is a core issue in automated map production, which aims at deriving smaller-scale maps from larger-scale maps by utilizing transformation operations [1,2,3]
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 objective of this study is to develop an operator of typification aiming at solving the generalization issues for building groups with grid patterns

Summary

Introduction

Map generalization is a core issue in automated map production, which aims at deriving smaller-scale maps from larger-scale maps by utilizing transformation operations [1,2,3]. Building generalization is normally subdivided into two steps: building grouping and generalization execution [6]. Building grouping is the process of arranging individual buildings into clusters by considering their similarities and differences. Generalization execution denotes the selection of appropriate operators to generalize the grouped building clusters. The most frequently used operators for building generalization are elimination, collapse, simplification, aggregation, typification, exaggeration, and displacement. These operators have different functions in diverse situations. For an unstructured building cluster, aggregation is normally implemented, while, in a regular linear building group, typification is considered as an appropriate operator

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