Extracting building patterns with multilevel graph partition and building grouping

Du, Shihong; Luo, Liqun; Cao, Kai; Shu, Mi

doi:10.1016/j.isprsjprs.2016.10.001

Cited by 50 publications

(27 citation statements)

References 22 publications

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“…GP:m in Spatial properties of polygons cannot provide sufficient information to depict similarities between them. Hence, other spatial properties, like distance and connectivity, are indispensable [36]. However, both distance and connectivity represent the relation between polygons, which cannot be depicted by the IFS-IBA approach directly.…”

Section: Eifs-iba Similarity Approachmentioning

confidence: 99%

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Chen

et al. 2019

IJGI

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Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition.

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Section: Eifs-iba Similarity Approachmentioning

confidence: 99%

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Chen

et al. 2019

IJGI

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“…As the most common geographical entities in urban areas, buildings are important directional objects for users when using maps for navigation. The linear pattern formed by buildings refers to the arrangement and the form exhibited by a collection of buildings at a certain scale in the mapping space [1,2]. The pattern looks like a line, and its elements, i.e., buildings, are homogeneous in terms of spatial properties (e.g., spacing, orientation, shape, and density) [3].…”

Section: Introductionmentioning

confidence: 99%

“…The pattern looks like a line, and its elements, i.e., buildings, are homogeneous in terms of spatial properties (e.g., spacing, orientation, shape, and density) [3]. Typically, linear building patterns can be categorized into collinear alignments and curvilinear alignments [1,3]. As landscape configuration, building patterns are crucial components of urban structures, which have to be preserved when spatial scales decrease during the process of map generalization [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Graph-based grouping methods are the most common approaches, which in general, first model the building block as a graph in which nodes represent buildings and edges denote the adjacent relationships between buildings [12][13][14][15]. The graph is then segmented to obtain homogeneous groups by means of segmentation methods [1,16,17]. Some tracking methods can be integrated into the segmentation process to obtain linear building patterns [3,18].…”

Section: Introductionmentioning

confidence: 99%

“…Practice has proven that the mean distance metric proposed by [22] can more accurately measure the distance between neighboring objects than other distance metrics and has been widely used in many applications [1,6,12]. Similarity, including shape similarity and size similarity, is often used to develop rigid rules for detecting building patterns [1,3,24]. However, some objects with large differences in area and shape are still recognized as a whole, such as linear patterns.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing Linear Building Patterns in Topographic Data by Using Two New Indices based on Delaunay Triangulation

Deng

Luo

2020

IJGI

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Building pattern recognition is fundamental to a wide range of downstream applications, such as urban landscape evaluation, social analyses, and map generalization. Although many studies have been conducted, there is still a lack of satisfactory results, due to the imprecision of the relative direction model of any two adjacent buildings and the ineffective extraction methods. This study aims to provide an alternative for quantifying the direction and the spatial continuity of any two buildings on the basis of the Delaunay triangulation for the recognition of linear building patterns. First, constrained Delaunay triangulations (CDTs) are created for all buildings within each block and every two adjacent buildings. Then, the spatial continuity index (SCI), the direction index (DI), and other spatial relations (e.g., distance) of every two adjacent buildings are derived using the CDT. Finally, the building block is modelled as a graph based on derived matrices, and a graph segmentation approach is proposed to extract linear building patterns. In the segmentation process, the edges of the graph are removed first, according to the global thresholds of the SCI and distance, and are subsequently subdivided into subgraphs on direction rules. The proposed method is tested using three datasets. The experimental results suggest that the proposed method can recognize both collinear and curvilinear building patterns, given that the correctness values are all above 92% for the three study areas. The results also demonstrate that the novel SCI can effectively filter many insignificant neighbor relationships in the graph segmentation process. It is noteworthy that the proposed DI is capable of measuring building relative directions accurately and works efficiently in linear building pattern extraction.

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Measuring the similarity of building patterns using Graph Fourier transform

Chen

et al. 2021

Earth Sci Inform

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Extracting building patterns with multilevel graph partition and building grouping

Cited by 50 publications

References 22 publications

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Recognizing Linear Building Patterns in Topographic Data by Using Two New Indices based on Delaunay Triangulation

Measuring the similarity of building patterns using Graph Fourier transform

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