A Discrete Model for Topological Relationships between Uncertain Spatial Objects

Tøssebro, Erlend; Nygård, Mads

doi:10.1007/3-540-26772-7_30

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Another model for representing uncertainty in spatial database is introduced by Tossebro et al in [22][23][24][25][26]. In [23] the authors propose a representation of spatial data through uncertain points, uncertain lines and uncertain regions.…”

Section: Related Workmentioning

confidence: 98%

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A framework for integrating multi-accuracy spatial data in geographical applications

Belussi

Migliorini

2011

Geoinformatica

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In recent years the integration of spatial data coming from different sources has become a crucial issue for many geographical applications, especially in the process of building and maintaining a Spatial Data Infrastructure (SDI). In such context new methodologies are necessary in order to acquire and update spatial datasets by collecting new measurements from different sources. The traditional approach implemented in GIS systems for updating spatial data does not usually consider the accuracy of these data, but just replaces the old geometries with the new ones. The application of such approach in the case of an SDI, where continuous and incremental updates occur, will lead very soon to an inconsistent spatial dataset with respect to spatial relations and relative distances among objects. This paper addresses such problem and proposes a framework for representing multi-accuracy spatial databases, based on a statistical representation of the objects geometry, together with a method for the incremental and consistent update of the objects, that applies a customized version of the Kalman filter. Moreover, the framework considers also the spatial relations among objects, since they represent a particular kind of observation that could be derived from geometries or be observed independently in the real world. Spatial relations among objects need also to be compared in spatial data integration and we show that they are necessary in order to obtain a correct result in merging objects geometries.

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Section: Related Workmentioning

confidence: 98%

“…In [24] the authors extend their model with some constructs for representing also temporal uncertainty into a spatial database. Finally, in [26] the model is completed with the representation of topological relationships between uncertain spatial objects, since they cannot be directly inferred from the object representations.…”

Section: Related Workmentioning

confidence: 99%