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• W2965227112 startingPage "333" @default.
• W2965227112 abstract "Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone." @default.
• W2965227112 created "2019-08-13" @default.
• W2965227112 creator A5074590287 @default.
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• W2965227112 date "2019-07-27" @default.
• W2965227112 modified "2023-10-16" @default.
• W2965227112 title "Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension" @default.
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• W2965227112 doi "https://doi.org/10.3390/ijgi8080333" @default.
• W2965227112 hasPublicationYear "2019" @default.
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