Data systems for 3D Geospatial Analytics in collaborative Extended Reality (XR)

dc.contributor.advisorRay, Suprio
dc.contributor.authorSharma, Vidit
dc.date.accessioned2024-06-04T18:40:58Z
dc.date.available2024-06-04T18:40:58Z
dc.date.issued2024-03
dc.description.abstractThe rise in 3D spatial data necessitates efficient management. Extended Reality (XR) merges physical and digital realms, promising immersive visualization and revolutionizing geospatial systems. This project investigates scalable data systems for collaborative 3D spatial XR applications crucial for disaster readiness. It optimizes immersive virtual environments and object organization within the Metaverse using Java-based software for spatial indices. Scrutinizing Degree of Visibility (DoV) computation methods enhances urban planning, gaming, and social interaction in VR. The HDoV-tree structure improves visual realism and performance, outperforming R-tree-based systems. Additionally, Semantic Textual Similarity using BERT enhances spatial data analysis, integrating advanced NLP techniques. Integrating 3D City Database (3DCityDB) facilitates efficient storage, analysis, and visualization of urban environments. In conclusion, this project amalgamates spatial data management advancements, immersive visualization, and optimized data structures, propelling Spatial Extended Reality for enhanced collaborative experiences across domains.
dc.description.copyright© Vidit Sharma, 2024
dc.format.extentxiii, 92
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37911
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineManagement
dc.titleData systems for 3D Geospatial Analytics in collaborative Extended Reality (XR)
dc.typemaster report
oaire.license.conditionother
thesis.degree.disciplineManagement
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.B.A.

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