A Qualitative Model for Visibility Relations

Tarquini, Francesco; Felice, Giorgio De; Fogliaroni, Paolo; Clementini, Eliseo

doi:10.1007/978-3-540-74565-5_52

Cited by 22 publications

(13 citation statements)

References 5 publications

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“…Spatial operators that are invoked by functions can be distinguished in those already implemented in OGC standards, such as topological relations, Euclidean distance, set operations, convex hull, and many others, and those for which it does not exist a standard implementation: regarding this latter group, we implemented our own version of operators (some of them are listed in Table 1). For more information on spatial operators, it is possible to refer to the broad literature on them, such as topological relations [20,21], projective relations [22]- [26], directional relations [27], and visibility relations [28]- [31].…”

Section: B Methodologymentioning

confidence: 99%

Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

Clementini

Natali

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Land cover units are aggregations of land cover components that are obtained by using criteria of homogeneity and proximity of basic components. For example, residential urban settlements can be defined as aggregations of single buildings, neighboring green spaces, paved surfaces and small roads, which are separated by more prominent land cover components, such as main roads or rivers. Land cover components belong to standard classes typically obtained by an automated classification process applied to aerial or satellite images, such as buildings, constructed areas, bare soil, water, vegetation, and the like. Land cover units belong to more general classes, obtained by a combination of land cover components, such as residential areas, industrial areas, road networks, river systems, and agricultural units. In this paper, we describe an approach based on the application of geometric rules and semantic constraints to extract land cover units from land cover components. We use spatial operators to extract composite land cover units from land cover databases, where spatial operators are taken from standards of the Open Geospatial Consortium. Expert knowledge needs to be translated into specific automatic procedures, called complex object definitions or CODs. Finally, we build a prototype system, where the user can choose among a set of available CODs to build a sequence of actions that lead to the discovery of knowledge. We discuss several study cases, such as the recognition of urban settlements, agricultural land units, and road networks.

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Section: B Methodologymentioning

confidence: 99%

Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

Clementini

Natali

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…For the sake of clarity, the point size is exaggerated Table 3 Summary of topological relations for all models and for all dimensions between simple objects Table 4 Number of topological relations between two complex spatial objects for the 9IM tion of few contributions about topological relations [34] and projective relations [16]. In urban applications, it is important to examine 3D objects that are anchored to the Earth's surface with an added vertical direction: work on visibility relations between such objects has been preliminary carried out in [41,43,64].…”

Section: Dimension Of the Embedding Spacementioning

confidence: 99%

A Conceptual Framework for Modelling Spatial Relations

Clementini¹

2019

ITC

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Various approaches lie behind the modelling of spatial relations, which is a heterogeneous and interdisciplinary field. In this paper, we introduce a conceptual framework to describe the characteristics of various models and how they relate each other. A first categorization is made among three representation levels: geometric, computational, and user. At the geometric level, spatial objects can be seen as point-sets and relations can be formally defined at the mathematical level. At the computational level, objects are represented as data types and relations are computed via spatial operators. At the user level, objects and relations belong to a context-dependent user ontology. Another way of providing a categorization is following the underlying geometric space that describes the relations: we distinguish among topologic, projective, and metric relations. Then, we consider the cardinality of spatial relations, which is defined as the number of objects that participate in the relation. Another issue is the granularity at which the relation is described, ranging from general descriptions to very detailed ones. We also consider the dimension of the various geometric objects and the embedding space as a fundamental way of categorizing relations.

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“…The qualitative spatial aspect refers to changes in the qualitative spatial relations holding between a feature and its neighbors. According to Clementini (2013), qualitative spatial relations belong to one of three classes: topological (Egenhofer 1989;Randell and Cohn 1989;Tarquini and Clementini 2008), projective (Clementini et al 2010;Tarquini et al 2007;Fogliaroni and Clementini 2015;Billen and Clementini 2006, 2005b, 2005a, and metric (Hernández, Clementini, and Di Felice 1995;Moratz and Ragni 2008).…”

Section: Qualitative Spatial Aspectmentioning

confidence: 99%

Data trustworthiness and user reputation as indicators of VGI quality

Fogliaroni

D’Antonio

Clementini

2018

Geo-spatial Information Science

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Volunteered geographic information (VGI) has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations. But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge. This article first formulates the problem of quality assessment of VGI data. Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric, qualitative, and semantic aspects of edits over time. An implementation of the model is running on a small data-set for a preliminary empirical validation. The results indicate that the computed trustworthiness provides a valid approximation of VGI quality. ARTICLE HISTORY

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A Qualitative Model for Visibility Relations

Cited by 22 publications

References 5 publications

Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

A Conceptual Framework for Modelling Spatial Relations

Data trustworthiness and user reputation as indicators of VGI quality

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