Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data

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Schulze, M.J.; Thiemann, F.; Sester, M.: Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (2014), S. 15-22. DOI:

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Sum total of downloads: 89

In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If both datasets are maintained separately, different capturing methods and intervals leads to inconsistencies in geometry and semantic, even if the same reality has been modelled. Our project aims to automatically harmonize such datasets and to allow an efficient actualisation of the semantics. The application domain in our project is cadastral and topographic datasets. To resolve geometric conflicts between topographic and cadastral data a local nearest neighbour method was used to identify perpendicular distances between a node in the topographic and an edge in the cadastral dataset. The perpendicular distances are reduced iteratively in a constraint least squares adjustment (LSA) process moving the coordinates from node and edge towards each other. The adjustment result has to be checked for conflicts caused by the movement of the coordinates in the LSA. The correct choice of matching partners has a major influence on the result of the LSA. If wrong matching partners are linked a wrong adaptation is derived. Therefore we present an improved matching method, where we take distance, orientation and semantic similarity of the neighbouring objects into account. Using Machine Learning techniques we obtain corresponding land-use classes. From these a measurement for the semantic distance is derived. It is combined with the orientation difference to generate a matching probability for the two matching candidates. Examples show the benefit of the proposed similarity measure.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2014
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 65 73.03%
2 image of flag of United States United States 10 11.24%
3 image of flag of China China 5 5.62%
4 image of flag of Nepal Nepal 2 2.25%
5 image of flag of Taiwan Taiwan 1 1.12%
6 image of flag of Turkey Turkey 1 1.12%
7 image of flag of Netherlands Netherlands 1 1.12%
8 image of flag of Malaysia Malaysia 1 1.12%
9 image of flag of Korea, Republic of Korea, Republic of 1 1.12%
10 image of flag of Algeria Algeria 1 1.12%
    other countries 1 1.12%

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