Mesh simplification for building typification

Burghardt, Dirk; Cecconi, Alesandro

doi:10.1080/13658810600912323

Cited by 53 publications

(42 citation statements)

References 13 publications

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“…5 right) to represent proximity relations for managing the building displacement process. Thus triangulations can be used for expressing neighbourhood and proximity relations as shown by Burghardt and Cecconi (2007) for the typification of buildings.…”

Section: Triangulations and Related Structuresmentioning

confidence: 99%

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Web service approaches for providing enriched data structures to generalisation operators

Neun

Burghardt

Weibel

2008

International Journal of Geographical Information Science

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Web service approaches for providing enriched data structures to generalisation operators Abstract Web service technologies can be used to establish an interoperable framework between different generalisation systems. In a previous article three categories of generalisation web services were identified, including support services, operator services and processing services. This paper focuses on the category of support services. In a service-based generalisation system, the purpose of support services is to assist the generalisation process by providing auxiliary measures, procedures and data structures that allow the representation of structural cartographic knowledge. The structural knowledge of the spatial and semantic context and the modelling of structural and spatial relationships is critical for the understanding of the role of cartographic features and thus for automated generalisation. Support services should extract and model this knowledge from the raw data and make it available to other generalisation operators. On the one hand the structural knowledge can be expressed by enriching map features with additional geometries or attributes. On the other hand, there exist various hierarchical and nonhierarchical relationships between map features, many of which can be represented by graph data structures. After a brief introduction to the interoperable web service framework, this paper proposes a taxonomy of generalisation support services and discusses its elements. It is then shown how the complex output of such services can be represented for use with web services and stored in a reusable fashion. Finally, the utilisation of support services is illustrated on four implementation examples of support services that also highlight the interactions with the generalisation operators that use these auxiliary services. Web service technologies can be used to establish an interoperable framework between different generalisation systems. In a previous article three categories of generalisation web services have been identified, including support services, operator services and processing services. This paper focuses on the category of support services. In a service-based generalisation system, the purpose of support services is to assist the generalisation process by providing auxiliary measures, procedures and data structures that allow to represent structural cartographic knowledge. The structural knowledge of the spatial and semantical context and the modelling of structural and spatial relationships is critical for the understanding of the role of cartographic features and thus for automated generalisation. Support services should extract and model this knowledge from the raw data and make it available to other generalisation operators. On the one hand the structural knowledge can be expressed by enriching map features with additional geometries or attributes. On the other hand there exist various hierarchical and non-hierarchical relationships between map features, many of which can be represented by graph da...

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Section: Triangulations and Related Structuresmentioning

confidence: 99%

“…Hence, these two operators are first briefly explained. The typification algorithm adopts the idea of Burghardt and Cecconi (2007) and uses the Delaunay triangulation of the building centroids supplied by the support service (Fig. 10) for deriving which buildings are closest to each other.…”

Section: Proximity Relations For Building Generalisationmentioning

confidence: 99%

Web service approaches for providing enriched data structures to generalisation operators

Neun

Burghardt

Weibel

2008

International Journal of Geographical Information Science

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“…Hoppe et al (1993) referred that mesh optimization can be put to use effectively in mesh simplification (the reduction in the number of vertices in an initially dense mesh of triangles). Burghardt et al (2007) used the mesh simplification for the typification of buildings.…”

Section: Mesh Simplificationmentioning

confidence: 99%

Delaunay Triangulation Parallel Construction Method and Its Application in Map Generalization

Shen

Guo

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Delaunay triangulated irregular network (D-TIN) has been widely used in various fields and also played an increasingly important role on map generalization. But for massive data processing, current D-TIN algorithm is still not efficient enough to meet the requirements of map generalization. Data partitioning is an important step of parallel algorithm design. The load balance and efficiency of data partitioning is the precondition of improving parallel algorithm efficiency. For aggregated distributed point sets, the traditional Delaunay Triangulation parallel algorithm can't ensure the balance and the execution's efficiency of the partitioning result. The paper introduces a partitioning method using dynamic strips aiming to guarantee the computing load balance. We tested the speed-up of the D-TIN parallel algorithm using different type of point sets and the results of the experiments shows that the method of dynamic strips partitioning can help to get high and stable speed-up and the data distributional pattern and size has less influence to it. The paper realizes a mesh simplification algorithm based on parallel D-TIN and compares the efficiency based on parallel and serial D-TIN.

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“…In particular, the enriched information can be used to facilitate map object grouping in support of meaningful aggregation and other operators. For example, it can be used to improve building typification [6] and displacement algorithms [4], by increasing the weights of buildings belonging to certain patterns. In addition, building patterns are useful in much broader fields, such as spatial analysis, database enrichment for semantic interoperation, information retrieval, etc.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Stoter

et al. 2011

Geoinformatica

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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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Mesh simplification for building typification

Cited by 53 publications

References 13 publications

Web service approaches for providing enriched data structures to generalisation operators

Web service approaches for providing enriched data structures to generalisation operators

Delaunay Triangulation Parallel Construction Method and Its Application in Map Generalization

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

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