Relative positions in words: a system that builds descriptions around Allen relations

Matsakis, Pascal; Wawrzyniak, Lukasz; Ni, Jingbo

doi:10.1080/13658810802270587

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…9d) are examples of orientation relations. Position relations contain relative position relations (Papadias & Theodoridis 1997, Matsakis et al 2008 (Fig. 9f), that can be specific such as the relative position of buildings with dead ends proposed by Duchêne et al (2012) (Fig.…”

Section: A Taxonomy Of Spatial Relations For Generalisationmentioning

confidence: 99%

Modelling Geographic Relationships in Automated Environments

Touya

Bucher

Falquet

et al. 2014

Lecture Notes in Geoinformation and Cartography

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Automated processes such as cartographic generalisation require formal abstraction of the geographic space in order to analyse, process and transform it. Spatial relations are key to understanding geographic space and their modelling is a critical issue. This chapter reports on existing classifications and modelling frameworks for spatial relations. A generic model is proposed for building an ontology of spatial relations for automatic processes such as generalisation or ondemand mapping, with a focus on so-called multiple representation relations. Propositions to use such ontology in an automated environment are reported. The three use cases of the chapter describe recent research that uses relations modelling. The first use case is the extension of CityGML with relations for 3D city models. The second use case presents the use of spatial relations for automatic spatial analysis, and particularly the grouping of natural features such as lakes or islands. Finally, the third use case is a data migration model guided by relations that govern the positioning of thematic data upon changing reference data.

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Section: A Taxonomy Of Spatial Relations For Generalisationmentioning

confidence: 99%

Modelling Geographic Relationships in Automated Environments

Touya

Bucher

Falquet

et al. 2014

Lecture Notes in Geoinformation and Cartography

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“…In this situation, people hope to express these five parts separately as five fuzzy regions to provide an adequate spatial description and an understanding of the home range of birds. From the aspect of spatial relationships, a fuzzy region object with several core areas and a connected boundary may not provide a satisfactory fine spatial relationship description, but such descriptions are useful in the interpretation of spatial relations (Du, Wang, & Qin, 2006; Du, Wang, Feng, & Yang, 2017; Matsakis, Keller, Wendling, Marjamaa, & Sjahputera, 2001; Matsakis, Wawrzyniak, & Ni, 2010; Wang, Liu, Zhang, & Liu, 2014). Some researchers doubt that the existing fuzzy spatial object models are appropriate for representing vague region footprints (Wu et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Building fuzzy areal geographical objects from point sets

Guo

2021

Transactions in GIS

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Representations of fuzzy geographic objects and analyses of their spatial relationships have received considerable attention in the GIS and spatial database domains over the past 30 years. However, building fuzzy geographical objects from real data is still a challenge. Simple fuzzy areal object models are too restrictive for many applications, and general fuzzy areal models may not be restrictive enough; as a result, the extent of fuzzy regions that satisfy the relevant conditions may be too large to affect the location description and spatial analysis. A condition by which the number of cores is not greater than one is added for the general object model, and an operable method for constructing fuzzy objects from the point set is proposed. Two peak and pass sets are determined for the membership surface by the fuzzy morphometric analysis method. The first set is used to initially divide the footprint of the fuzzy surface into smaller subfootprints, and the second set is used to merge insignificant fuzzy objects with their nearest significant fuzzy objects; thus, unreasonable division is avoided. Cross‐validation is adopted to evaluate the generated fuzzy objects. An experiment is provided to verify the effectiveness of the proposed method.

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“…For this purpose each pair of 2D objects is decomposed into parallel segments in every direction and use 1D Allen relations. Matsakis et al [27,28] used this sort of relations but they use a selected combination of Allen relations and two different systems for modeling the topological and directional relations.…”

Section: Related Workmentioning

confidence: 99%

Two-Dimensional Fuzzy Spatial Relations: A New Way of Computing and Representation

Salamat

Zahzah

2012

Advances in Fuzzy Systems

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Fuzziness is found everywhere, in modeling spatial relations, fuzziness is found at object level as well as in relation semantics. Commonly, fuzzy topological relations are computed between fuzzy objects. Fuzziness in relation semantics is represented by fuzzy topological relations between crisp objects and these types of fuzzy topological relations are much less developed. In this paper, we propose a method for combining fuzzy topological and directional relations. We also propose an algorithm for defuzzification of relations which provides us a binary topological and directional relation between a 2D object pair. These relations are represented in a neighborhood graph. For validation and assessment, a number of experiments have been performed on artificial data.

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Relative positions in words: a system that builds descriptions around Allen relations

Cited by 12 publications

References 30 publications

Modelling Geographic Relationships in Automated Environments

Modelling Geographic Relationships in Automated Environments

Building fuzzy areal geographical objects from point sets

Two-Dimensional Fuzzy Spatial Relations: A New Way of Computing and Representation

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