Dimensionally Extended Nine-Intersection Model (DE-9IM)

Strobl, Christian

doi:10.1007/978-3-319-17885-1_298

Cited by 10 publications

(10 citation statements)

References 7 publications

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“…To detect and compute SIFT descriptors, we used the OpenCV 2 library. Furthermore, we used the Shapely 3 package for Python to calculate concrete spatial relations between object-polygons in each scene, based on the DE-9IM model [23]. Moreover, for computing the optimal solution of an HC encoding, we utilized CLASP 3.1.2 and GRINGO 4.5.1 [7].…”

Section: Discussionmentioning

confidence: 99%

Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

Eiter

Kaminski

2016

Logics in Artificial Intelligence

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We consider the problem of classifying visual objects in a scene by exploiting the semantic context. For this task, we define hybrid classifiers (HC) that combine local classifiers with context constraints, and can be applied to collective classification problems (CCPs) in general. Context constraints are represented by weighted ASP constraints using object relations. To integrate probabilistic information provided by the classifier and the context, we embed our encoding in the formalism LP M LN , and show that an optimal labeling can be efficiently obtained from the corresponding LP M LN program by employing an ordinary ASP solver. Moreover, we describe a methodology for constructing an HC for a CCP, and present experimental results of applying an HC for object classification in indoor and outdoor scenes, which exhibit significant improvements in terms of accuracy compared to using only a local classifier.

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Section: Discussionmentioning

confidence: 99%

Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

Eiter

Kaminski

2016

Logics in Artificial Intelligence

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“…If our focal data depict streets as line geometries, we can request which neighborhoods these streets intersect. Generally, there is a multitude of more possibilities of formulating such requests on georeferenced data (e.g., Strobl, 2017) that also depend on research questions within specific scientific disciplines as shown below.…”

Section: What Is Spatial Linking?mentioning

confidence: 99%

An Infrastructure for Spatial Linking of Survey Data

Bensmann

Heling

Jünger

et al. 2020

Data Science Journal

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Research on environmental justice comprises health and well-being aspects, as well as topics related to general social participation. In this research field, among others, there is a need for an integrated use of social science survey data and spatial science data, e.g. for combining demographic information from survey data with data on pollution from spatial data. However, for researchers it is challenging to link both data sources, because (1) the interdisciplinary nature of both data sources is different, (2) both underlie different legal restrictions, in particular regarding data privacy, and (3) methodological challenges arise regarding the use of geo-information systems (GIS) for the processing and analysis of spatial data.In this article, we present an infrastructure of distributed web services which supports researchers in the process of spatial linking. The infrastructure addresses the challenges researchers have to face during that process. We present an example case study on the investigation of environmental inequalities with regards to income and land use hazards in Germany by using georeferenced survey data of the GESIS Panel and the German Socio-economic Panel (SOEP), and by using spatial data from the Monitor of Settlement and Open Space Development (IOER Monitor). The results show that increasing income of survey respondents is associated with less exposure to land-use-related environmental hazards in Germany.

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“…The Dimensionally Extended 9-Intersection Model (DE-9IM) (Strobl, 2008) is a full descriptive assertion about two spatial objects in 2 R . The "9-Intersection" model belongs to binary classification.…”

Section: The Dimensionally Extended Modelmentioning

confidence: 99%

Extension of RCC Topological Relations for 3d Complex Objects Components Extracted From 3d Lidar Point Clouds

Xing¹,

Mostafavia²,

Wang³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in 3 R . Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in 3 R . We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.* Corresponding author INTRDUCTIONSpatial relations include topological, metric and directional relations and together with semantic information are used for describing a scene qualitatively (Mark, 1994). Topological relations between geographical objects are necessary for spatial analysis in GIS. These relations can be queried and analysed independently from geographic coordinate system definition and the specific location of objects. Topological relations describe relative spatial relations with respect to reference objects. Hence topological relations are invariant and do not change with topological transformations, such as translation, scaling, and rotation (Egenhofer, 1990b).In general, topological relations between spatial objects are derived from Region Connection Calculus (RCC-8) (Egenhofer, 1989;Egenhofer, 1991) Here, a region is defined as a 2-cell with a non-empty, connected interior (Egenhofer, 1990a). Additionally, the 4-Intersection Model (4IM) (Egenhofer, 1991), 9-Intersection Model(9IM) (Clementini, 1993) and Dimensionally Extended models (DE) (Clementini, 1993) are widely adopted and implemented for describing topological relations for spatial analysis. Topological relations between spatial objects can be described based on relations defined for 2D regions in RCC model. Basic relations between two regions include disjoint, meet, overlap, contain, cover, coveredBy, containedBy and equal (Egenhofer, 1990b;Randell, 1992).The definitions of topological relations between spatial objects in 3 R are closely related to 3D objects models. A 3D spatial object can be modelled as a solid geometry or represented by its boundaries. Thus, topological relations between spatial objects in 3 R can be divid...

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Dimensionally Extended Nine-Intersection Model (DE-9IM)

Cited by 10 publications

References 7 publications

Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

An Infrastructure for Spatial Linking of Survey Data

Extension of RCC Topological Relations for 3d Complex Objects Components Extracted From 3d Lidar Point Clouds

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