An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Chen, Zhanlong; Ma, Xiaochuan; Wu, Liang; Xie, Zhong

doi:10.3390/ijgi8020098

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…The setting of values in the decision-making process can be changed depending on the accuracy and reliability of the input data. If it is necessary to include the uncertainty of the data in spatial analyses, we propose applying the principles of fuzzy set theory [10,80,81].…”

Section: Shape Similarity Index Calculation and Objects Classificationmentioning

confidence: 99%

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Ďuračiová

2023

IJGI

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The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control.

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Section: Shape Similarity Index Calculation and Objects Classificationmentioning

confidence: 99%

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Ďuračiová

2023

IJGI

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“…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%

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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An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Cited by 7 publications

References 30 publications

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

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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