Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. However, these powerful methods often fail to perform pattern analysis and knowledge mining with spatial vector data because in most cases, such data are not underlying grid-like or array structures but can only be modeled as graph structures. The present study introduces a novel graph convolution by converting it from the vertex domain into a point-wise product in the Fourier domain using the graph Fourier transform and convolution theorem. In addition, the graph convolutional neural network (GCNN) architecture is proposed to analyze graph-structured spatial vector data. The focus of this study is the classical task of building pattern classification, which remains limited by the use of design rules and manually extracted features for specific patterns. The spatial vector data representing grouped buildings are modeled as graphs, and indices for the characteristics of individual buildings are investigated to collect the input variables. The pattern features of these graphs are directly extracted by training labeled data. Experiments confirmed that the GCNN produces satisfactory results in terms of identifying regular and irregular patterns, and thus achieves a significant improvement over existing methods. In summary, the GCNN has considerable potential for the analysis of graph-structured spatial vector data as well as scope for further improvement.

A graph convolutional neural network for classification of building patterns using spatial vector data

This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the simplification by searching and matching the most similar template that can be used later to replace the original building. On the premise of satisfying the individual geometric accuracy on a smaller scale, the proposed method can enhance the impression of well-known landmarks and reflect the pattern in mapping areas by the symbolic template. The turning function that describes shape by measuring the changes of the tangent-angle as a function of the arc-length is employed to obtain the similar distance between buildings and template polygons, and the least squares model is used to control the geometry matching of the candidate template. Experiments on real datasets are carried out to assess the usefulness of this method and compare it with two existing methods. The experiments suggest that our method can preserve the main structure of building shapes and geometric accuracy.

Template Matching and Simplification Method for Building Features Based on Shape Cognition

Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrati...

A graph deep learning approach for urban building grouping

In map generalization, scale reduction and feature symbolization inevitably generate problems of overlapping objects or map congestion. To solve the legibility problem with respect to the generalization of dispersed rural buildings, selection of buildings is necessary and can be transformed into an optimization problem. In this paper, an improved genetic algorithm for building selection is designed to be able to incorporate cartographic constraints related to the building selection problem. Part of the local constraints for building selection is used to constrain the encoding and genetic operation. To satisfy other local constraints, a preparation phase is necessary before building selection, which includes building enlargement, local displacement, conflict detection, and attribute enrichment. The contextual constraints are used to ascertain a fitness function. The experimental results indicate that the algorithm proposed in this article can obtain good results for building selection whilst preserving the spatial distribution characteristics of buildings.

https://www.mdpi.com/2220-9964/6/9/271/pdf

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

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.

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

This paper presents a typification method for linear pattern in urban building generalization. The proposed method includes two processes. Firstly, structural knowledge in terms of linear pattern is detected using a two-step algorithm taking the advantages of Gestalt visual perception, computational geometry and graph theory. Spatial neighbourhood is captured using interpolated constrained Delaunay triangulation and the resulting proximity graph is pruned to be heterogeneous to get acceptable linear patterns with regard to Gestalt visual perception. Then, a typification strategy is proposed, in which typification is regarded as a progressive and iterative process consisting of elimination, exaggeration and displacement. The typification strategy iteratively executes eliminating the building with minimum overall effect, exaggerating remaining buildings considering key location and spatial characteristics and displacing them to preserve the linear pattern until elimination quantity is satisfied. Exp...

A typification method for linear pattern in urban building generalisation

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A typification method for linear pattern in urban building generalisation