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Seth Teller. Battat, Jonathan Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2011-02-23T14:40:43Z 2011-02-23T14:40:43Z 2010 2010 http://hdl.handle.net/1721.1/61278 702366351 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 110-112). This thesis describes a system and method for extending the current paradigm of geographic information systems (GIS) to support indoor environments. It introduces features and properties of indoor multi-building environments that do not exist in other geographic environments or are not characterized in existing geospatial models, and proposes a comprehensive representation for describing such spatial environments. Specifically, it presents enhanced notions of spatial containment and graph topology for indoor environments, and extends existing geometric and semantic constructs. Furthermore, it describes a framework to: automatically extract indoor spatial features from a corpus of semi-structured digital floor plans; populate the aforementioned indoor spatial representation with these features; store the spatial data in a descriptive yet extensible data model; and provide mechanisms for dynamically accessing, mutating, augmenting, and distributing the resulting large-scale dataset. Lastly, it showcases an array of applications, and proposes others, which utilize the representation and dataset to provide rich location-based services within indoor environments. by Jonathan Battat. M.Eng. 141 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. A fine-grained geospatial representation and framework for large-scale indoor environments Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9ce4331a-5086-4719-93f9-b189347daf1c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A fine-grained geospatial representation and framework for large-scale indoor environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Battat, Jonathan</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis describes a system and method for extending the current paradigm of geographic information systems (GIS) to support indoor environments. It introduces features and properties of indoor multi-building environments that do not exist in other geographic environments or are not characterized in existing geospatial models, and proposes a comprehensive representation for describing such spatial environments. Specifically, it presents enhanced notions of spatial containment and graph topology for indoor environments, and extends existing geometric and semantic constructs. Furthermore, it describes a framework to: automatically extract indoor spatial features from a corpus of semi-structured digital floor plans; populate the aforementioned indoor spatial representation with these features; store the spatial data in a descriptive yet extensible data model; and provide mechanisms for dynamically accessing, mutating, augmenting, and distributing the resulting large-scale dataset. Lastly, it showcases an array of applications, and proposes others, which utilize the representation and dataset to provide rich location-based services within indoor environments.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>