Characterization and Detection of Building Patterns in Cartographic Data: Two Algorithms

Zhang, Xiang; Ai, Tinghua; Stoter, J.E.

doi:10.1007/978-3-642-25926-5_8

Cited by 24 publications

(30 citation statements)

References 9 publications

(18 reference statements)

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“…1), buildings are regarded as vertices and any two buildings that share at least one triangle are regarded as proximal, and an edge forms between the centroids of these two buildings. The detailed instruction and discussion of proximity graph can be referred in references (Zhang et al 2010(Zhang et al , 2013. 2 Proximity graph refinement Proximity graph only reflects the topological proximity of buildings, even two distant buildings may have the proximity relationship, which has less benefits to the further analyzing.…”

Section: Proximity Graph Of Area Featurementioning

confidence: 99%

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

Xiao

Burghardt

2018

Proc. Int. Cartogr. Assoc.

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This paper presents a new strategy for the generalization of discrete area features by using stroke grouping method and polarization transportation selection. The mentioned stroke is constructed on derive of the refined proximity graph of area features, and the refinement is under the control of four constraints to meet different grouping requirements. The area features which belong to the same stroke are detected into the same group. The stroke-based strategy decomposes the generalization process into two sub-processes by judging whether the area features related to strokes or not. For the area features which belong to the same one stroke, they normally present a linear like pat-tern, and in order to preserve this kind of pattern, typification is chosen as the operator to implement the generalization work. For the remaining area features which are not related by strokes, they are still distributed randomly and discretely, and the selection is chosen to conduct the generalization operation. For the purpose of retaining their original distribution characteristic, a Polarization Transportation (PT) method is introduced to implement the selection operation. Buildings and lakes are selected as the representatives of artificial area feature and natural area feature respectively to take the experiments. The generalized results indicate that by adopting this proposed strategy, the original distribution characteristics of building and lake data can be preserved, and the visual perception is pre-served as before.

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Section: Proximity Graph Of Area Featurementioning

confidence: 99%

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

Xiao

Burghardt

2018

Proc. Int. Cartogr. Assoc.

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“…Regnault [12], Yan et al [13], Steinhauer et al [14] and Qi and Li [15] all proposed methods for the grouping of buildings. Zhang et al [16] proposed a categorization of building patterns which includes collinear, curvilinear, align-along-road, grid-like and unstructured. The detection of collinear building patterns has been extensively studied [17,18].…”

Section: Implicit Spatial Informationmentioning

confidence: 99%

“…The detection of collinear building patterns has been extensively studied [17,18]. Zhang et al [16] proposed algorithms for detecting align-along-road and unstructured building patterns. Luscher et al [19] demonstrated that higher level semantics, such as terraced house, can be derived from building alignments and other criteria.…”

Section: Implicit Spatial Informationmentioning

confidence: 99%

Spatial Relations Using High Level Concepts

Corcoran

Mooney

Bertolotto

2012

IJGI

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Existing models of spatial relations do not consider that different concepts exist on different levels in a hierarchy and in turn that the spatial relations in a given scene are a function of the specific concepts considered. One approach to determining the existence of a particular spatial relation is to compute the corresponding high level concepts explicitly using map generalization before inferring the existence of the spatial relation in question. We explore this idea through the development of a model of the spatial relation "enters" that may exist between a road and a housing estate.

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“…In addition, the computation of nearest distance between buildings and between buildings and roads on this structure becomes more efficient [33]. This step results in initial proximity graph.…”

Section: 2mentioning

confidence: 99%

“…clusters) [33], distinguishing between linear alignments (collinear, curvilinear and alignalong-road) and nonlinear clusters (grid-like and unstructured), see Fig. 1.…”

Section: Related Workmentioning

confidence: 99%

Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

Zhang

Stoter

et al. 2011

Geoinformatica

Self Cite

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Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.

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Characterization and Detection of Building Patterns in Cartographic Data: Two Algorithms

Cited by 24 publications

References 9 publications

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

Spatial Relations Using High Level Concepts

Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

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