An algebraic approach to automated geospatial information fusion

Duckham, Matt; Worboys, Michael

doi:10.1080/13658810500032339

Cited by 62 publications

(42 citation statements)

References 29 publications

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“…The problem under consideration resembles to some extent the problem described in (Duckham and Worboys 2005). Both the problem and solution are, however, completely different: the authors in (Duckham and Worboys 2005) combine different datasets that relate to the same area of interest in order to create a new dataset that has the combined information of both source data sets.…”

Section: Data Fusionmentioning

confidence: 99%

“…Both the problem and solution are, however, completely different: the authors in (Duckham and Worboys 2005) combine different datasets that relate to the same area of interest in order to create a new dataset that has the combined information of both source data sets. This combined information can be richer or have a higher accuracy.…”

Section: Data Fusionmentioning

confidence: 99%

“…The proposed methodology makes use of data analysis, geometric matching and mathematical connections to solve format mismatches, it does not consider the higher concepts that relate to the meaning of the data (e.g. using ontologies to match differently labelled data, as in (Duckham and Worboys 2005)). The proposed methodology is still in a very early stage of development.…”

Section: Introductionmentioning

confidence: 99%

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Solving the map overlay problem with a fuzzy approach

Verstraete

2014

Climatic Change

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The map overlay problem occurs when mismatched gridded data need to be combined, the problem consists of determining which portion of grid cells in one grid relates to the partly overlapping cells of the target grid. This problem contains inherent uncertainty, but it is an important and necessary first step in analysing and combining data; any improvement in achieving a more accurate relation between the grids will positively impact the subsequent analysis and conclusions. Here, a novel approach using techniques from fuzzy control and artificial intelligence is presented to provide a new methodology. The method uses a fuzzy inference system to decide how data represented in one grid can be distributed over another grid using any additionally available knowledge, thus mimicking the higher reasoning that we as humans would use to consider the problem.

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Section: Data Fusionmentioning

confidence: 99%

Section: Data Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving the map overlay problem with a fuzzy approach

Verstraete

2014

Climatic Change

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“…In general, the mapping between ontologies is not automated and relies on the application of domain expertise. Recent approaches, however, try to automate such a process of geospatial information fusion [4].…”

Section: Geographical Ontologiesmentioning

confidence: 99%

Spatial Knowledge-Based Applications and Technologies: Research Issues

Bertino

Damiani

2005

Lecture Notes in Computer Science

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“…Ontology is often used for representing a structured vocabulary [12], and the fusion of ontology-based geospatial information must face the problem of heterogeneous vocabularies [10]. This paper deals with terminology integration and discusses the merging of information provided by different sources using multiple space partitions, and expressed with more or less precise labels from the same ontology resulting from a preliminary alignment.…”

Section: Introductionmentioning

confidence: 99%

Fusing Uncertain Structured Spatial Information

Saint-Cyr¹,

Jeansoulin²,

Prade³

2008

Lecture Notes in Computer Science

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Spatial information associates properties to labeled areas. Space is partitioned into (elementary) parcels, and union of parcels constitute areas. Properties may have various level of generality, giving birth to a taxonomy of properties for a given universe of discourse. Thus, the set of properties pertaining to a conceptual taxonomy, as the set of areas and parcels, are structured by a natural partial order. We refer to such structures as ontologies. In fusion problems, information coming from distinct sources may be expressed in terms of different conceptual and/or spatial ontologies, and may be pervaded with uncertainty. Dealing with several conceptual (or spatial) ontologies in a fusion perspective presupposes that these ontologies be aligned. This paper introduces a basic representation format called attributive formula, which is a pair made of a property and a set of parcels (to which the property applies), possibly associated with a certainty level. Uncertain attributive formulas are processed in a possibilistic logic manner, augmented with a two-sorted characterization: the property may be true everywhere in an area, or at least true somewhere in the area. The fusion process combines the factual information encoded by the attributive formulas provided by the different sources together with the logical encoding of the conceptual and spatial ontologies (obtained after alignment). Then, inconsistency encountered in the fusion process may be handled by taking advantage of the existence of different fusion modes, or by relaxing when necessary a closed world-like assumption stating by default that what is true somewhere in an area may be also true everywhere in it (if nothing else is known). A landscape analysis toy example illustrates the approach.

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An algebraic approach to automated geospatial information fusion

Cited by 62 publications

References 29 publications

Solving the map overlay problem with a fuzzy approach

Solving the map overlay problem with a fuzzy approach

Spatial Knowledge-Based Applications and Technologies: Research Issues

Fusing Uncertain Structured Spatial Information

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