Integration of heterogeneous geospatial data in a federated database

Butenuth, Matthias; Gösseln, Guido v.; Tiedge, Michael; Heipke, Christian; Lipeck, Udo W.; Sester, Monika

doi:10.1016/j.isprsjprs.2007.04.003

Cited by 90 publications

(56 citation statements)

References 15 publications

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“…Among the proposed methods. we must highlight the studies of Gösseln and Sester [42] and Butenuth et al [43]. These authors match polygon datasets by using an iterative closest point algorithm that detects corresponding point pairs for two point sets derived from each contour of corresponding objects.…”

Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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Section: Intra-elements Matching (Vertex-to-vertex)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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“…It is also common in the scientific literature to see that snapping is being used with different types of datasets, e.g., cadastral boundaries (Siejka et al 2013;Zygmunt et al 2014), topographic datasets (Butenuth et al 2007), digital gazetteer (Hastings 2008), and census data (Schuurman et al 2006).…”

Section: Edge-matchingmentioning

confidence: 99%

Solving the horizontal conflation problem with a constrained Delaunay triangulation

Ledoux

Ohori

2016

J Geogr Syst

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Datasets produced by different countries or organisations are seldom properly aligned and contain several discrepancies (e.g., gaps and overlaps). This problem has been so far almost exclusively tackled by snapping vertices based on a user-defined threshold. However, as we argue in this paper, this leads to invalid geometries, is error-prone, and leaves several discrepancies along the boundaries. We propose a novel algorithm to align the boundaries of adjacent datasets. It is based on a constrained Delaunay triangulation to identify and eliminate the discrepancies, and the alignment is performed without moving vertices with a snapping operator. This allows us to guarantee that the datasets have been properly conflated and that the polygons are geometrically valid. We present our algorithm, our implementation (based on the stable and fast triangulator in CGAL), and we show how it can be used it practice with different experiments with real-world datasets. Our experiments demonstrate that our approach is highly efficient and that it yields better results than snapping-based methods.

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“…Implicit information derived from spatial data can be used to support many applications such as map generalisation (where this process is known as data enrichment) [6,7], improving the quality of user generated spatial data [8], matching spatial datasets [9] and updating data [10]. Walter and Luo [2] provide a classification of the different forms of implicit information that can be derived from spatial data.…”

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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Integration of heterogeneous geospatial data in a federated database

Cited by 90 publications

References 15 publications

A Polygon and Point-Based Approach to Matching Geospatial Features

A Polygon and Point-Based Approach to Matching Geospatial Features

Solving the horizontal conflation problem with a constrained Delaunay triangulation

Spatial Relations Using High Level Concepts

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