Knowledge-based Discovery of Transportation Object Properties by Fusing Multi-modal GIS Data

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dc.contributor.authorMaroun, Pedro Eiden_US
dc.contributor.authorMudur, Sudhiren_US
dc.contributor.authorPopa, Tiberiuen_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:26Z
dc.date.available2018-09-19T15:15:26Z
dc.date.issued2018
dc.description.abstract3D models of transportation objects like a road, bridge, underpass, etc. are required in many domains including military training, land development, etc. While remote sensed images and LiDaR data can be used to create approximate 3D representations, detailed 3D representations are difficult to create automatically. Instead, interactive tools are used with rather laborious effort. For example, the top commercial interactive model generator we tried required 94 parameters in all for different bridge types. In this paper, we take a different path.We automatically derive these parameter values from GIS (Geographic Information Systems) data, which normally contains detailed information of these objects, but often only implicitly. The framework presented here transforms GIS data into a knowledge base consisting of assertions. Spatial/numeric relations are handled through plug-ins called property extractors whose results get added to the knowledge base, used by a reasoning engine to infer object properties. A number of properties have to be extracted from images, and are dependent on the accuracy of computer vision methods. While a comprehensive property extractor mechanism is work in progress, . a prototype implementation illustrates our framework for bridges with GIS data from the real world. To the best of our knowledge, our framework is the first to integrate knowledge inference and uncertainty for extracting landscape object properties by fusing facts from multi-modal GIS data sources.en_US
dc.description.sectionheadersVisualization III and VR
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181220
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages153-161
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181220
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181220
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.subjectDescription logics
dc.subjectReasoning about belief and knowledge
dc.titleKnowledge-based Discovery of Transportation Object Properties by Fusing Multi-modal GIS Dataen_US
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