Qualitative spatial reasoning methodology to determine the particular domain of a set of geographic objects

Torres, Miguel; Pacheco, Eduardo Eloy Loza; Alhalabi, Wadee; Guzmán, Giovanni; Quintero, Rolando; Moreno-Ibarra, Marco

doi:10.1016/j.chb.2016.01.034

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Geographic Information Science, in particular, has been the field in which most QSTR models have been applied, for example, Allen's Temporal Interval Relations calculus (Allen 1983), Region Connection Calculus (RCC) (Randell, Cui, and Cohn 1992), 9-Intersection model (Egenhofer 1995), etc. In the literature, examples of research works in GIS can be found in which formal ontologies are defined taking qualitative spatial reasoning models into account: the spatio-temporal ontology for representing the changing of historical places developed by Hyvönen et al (2011); a high-level conceptual metamodel, based on QSR approaches that can be used in the remote sensing domain as a framework to formalize spatio-temporal knowledge (Pierkot et al 2013); a method to perform qualitative spatial reasoning in geospatial representations explicitly formalized by means of an application ontology proposed by Torres et al (2016). In other fields, such as robotics and computer vision, other approaches have also dealt with the challenge of building a description-logics ontology using qualitative spatial concepts for digital image categorization (see QImageOntology by Falomir et al 2011).…”

Section: Designing Ontologies For Remote Sensing Applicationsmentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

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The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts' expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

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Section: Designing Ontologies For Remote Sensing Applicationsmentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

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“…It is found the use of QSR in geographic applications, such as in the works of (SCHULTZ; AMOR, 2006;TORRES et al, 2016;WALLGRÜN, 2010), aiming at learning topological maps in unknown environments, methods to deal with ambiguity in topological information, in addition to human-computer and systems integration of geographic information.…”

Section: Cognitive Roboticsmentioning

confidence: 99%

Collaborative spatial reasoning for environment mapping using unmanned aerial vehicles

Secolo¹

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RESUMOO objetivo deste projeto é a investigação dos formalismos de raciocínio espacial existentes para sistemas colaborativos, a fim de interpretar uma cena a partir de múltiplos pontos de vista em tarefas de mapeamento de ambientes. Motivadas pela crescente necessidade de interação entre humanos e robôs, as teorias do Raciocínio Espacial Qualitativo (QSR) são integradas em um único formalismo para modelar as percepções de veículos aéreos não tripulados (VANTs) operados remotamente. As teorias qualitativas possibilitam a troca de informações entre humanos e agentes robóticos, para que possam realizar tarefas em missões colaborativas envolvendo objetivos de busca e monitoramento na agricultura, desastres naturais, tarefas de busca e resgate, entre outros. A combinação das teorias espaciais estudadas levou ao desenvolvimento de dois formalismos: o Cálculo de Intervalo LH e o Raciocínio Espacial Colaborativo. O Cálculo de Intervalo LH consiste na combinação do Cálculo de Conexões de Regiões com a Algebra de Intervalos de Allen para descrever as relações entre dois objetos de um ponto de vista aéreo. O Raciocínio Espacial Colaborativo combina o Cálculo de Direção Cardinal com o Cálculo do Intervalo LH para a tarefa de mapeamento do ambiente onde os agentes têm uma visão parcial da cena. VANTs equipados com câmeras são a plataforma utilizada para testar o formalismo deste projeto, captando imagens com uma visão parcial do ambiente, de diferentes direções de voo. Os resultados obtidos mostraram que os dois formalismos propostos tiveram sucesso na tarefa de mapeamento do ambiente.

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A fuzzy spatial description logic for the semantic web

Cheng

2020

J Ambient Intell Human Comput

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Qualitative spatial reasoning methodology to determine the particular domain of a set of geographic objects

Cited by 3 publications

References 27 publications

Ontologies to interpret remote sensing images: why do we need them?

Ontologies to interpret remote sensing images: why do we need them?

Collaborative spatial reasoning for environment mapping using unmanned aerial vehicles

A fuzzy spatial description logic for the semantic web

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