{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/124266"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/124266","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"Real-Time Multiresolution Management of Spatiotemporal Earth Observation Data Using DGGS","abstract":"Earth observation (EO) data play a crucial role in environmental monitoring, climate analysis, and other large-scale geospatial applications. However, the rapid growth in the volume, spatial resolution, and temporal frequency of modern EO datasets has made their storage, processing, and access increasingly more challenging. Existing data management approaches struggle with scalability, geometric distortion, and redundancy. These challenges are further compounded by temporal gaps caused and missing data, and the lack of continuous temporal representations, as well as inefficient support for multiresolution querying and interactive access over arbitrary regions of interest. Together, these limitations hinder the practical use of EO data in time-sensitive and large-scale applications. To address these challenges, this thesis adopts a triangular Discrete Global Grid System (DGGS) as the foundation for a spatiotemporal data management framework. The hierarchical and globally consistent structure of DGGS enables efficient multiresolution representation and indexing of spatial data, while minimizing the geometric distortion that plagues traditional raster grids. Building on this foundation, the proposed framework introduces a tensor-based multiresolution storage scheme coupled with a triangular wavelet scheme defined directly on the DGGS grid. To handle temporal gaps, continuous temporal approximation is achieved using reverse Chaikin subdivision and B-spline curve fitting, allowing the system to not only support smooth approximation and live updates, but also to significantly reduce storage requirements. Together, these components define a scalable framework for real-time multiresolution processing, storage, and retrieval of spatiotemporal raster data. The proposed framework is validated through a case study using data from the RADARSAT Constellation Mission, where experimental results demonstrate efficient encoding, dynamic retrieval, and interactive visualization of time-varying EO data for user-defined regions, while maintaining high data fidelity. Moreover, the framework's scalability and performance are further evaluated using two additional large-scale EO datasets; one spatially extensive dataset covering the entire Canadian region, and another temporally extensive dataset spanning nearly 7 years over Gull Lake, Alberta. The results highlight the framework's ability to handle large volumes of EO data while providing efficient storage and access capabilities.","abstract_html":"Earth observation (EO) data play a crucial role in environmental monitoring, climate analysis, and other large-scale geospatial applications. However, the rapid growth in the volume, spatial resolution, and temporal frequency of modern EO datasets has made their storage, processing, and access increasingly more challenging. Existing data management approaches struggle with scalability, geometric distortion, and redundancy. These challenges are further compounded by temporal gaps caused and missing data, and the lack of continuous temporal representations, as well as inefficient support for multiresolution querying and interactive access over arbitrary regions of interest. Together, these limitations hinder the practical use of EO data in time-sensitive and large-scale applications. To address these challenges, this thesis adopts a triangular Discrete Global Grid System (DGGS) as the foundation for a spatiotemporal data management framework. The hierarchical and globally consistent structure of DGGS enables efficient multiresolution representation and indexing of spatial data, while minimizing the geometric distortion that plagues traditional raster grids. Building on this foundation, the proposed framework introduces a tensor-based multiresolution storage scheme coupled with a triangular wavelet scheme defined directly on the DGGS grid. To handle temporal gaps, continuous temporal approximation is achieved using reverse Chaikin subdivision and B-spline curve fitting, allowing the system to not only support smooth approximation and live updates, but also to significantly reduce storage requirements. Together, these components define a scalable framework for real-time multiresolution processing, storage, and retrieval of spatiotemporal raster data. The proposed framework is validated through a case study using data from the RADARSAT Constellation Mission, where experimental results demonstrate efficient encoding, dynamic retrieval, and interactive visualization of time-varying EO data for user-defined regions, while maintaining high data fidelity. Moreover, the framework's scalability and performance are further evaluated using two additional large-scale EO datasets; one spatially extensive dataset covering the entire Canadian region, and another temporally extensive dataset spanning nearly 7 years over Gull Lake, Alberta. The results highlight the framework's ability to handle large volumes of EO data while providing efficient storage and access capabilities.","abstract_has_math":false,"creators":["Mirzai Golpayegani, Amir"],"institution":"Graduate Studies","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Samavati, Faramarz F."],"committee_chairs":[],"committee_members":["Runions, Adam Drew","Bonnell, Tyler R."],"year":2026,"date_issued":"2026-02-26","date_published":"2026-02-26","updated_at":"2026-07-24T01:30:27Z","subjects":["Big Data Management","Satellite Imagery","Discrete Global Grid Systems","Geospatial Data Infrastructure","Temporal Data Approximation","Multiresolution","Triangular Wavelets"],"languages":["en"],"rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. 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Existing data management approaches struggle with scalability, geometric distortion, and redundancy. These challenges are further compounded by temporal gaps caused and missing data, and the lack of continuous temporal representations, as well as inefficient support for multiresolution querying and interactive access over arbitrary regions of interest. Together, these limitations hinder the practical use of EO data in time-sensitive and large-scale applications. To address these challenges, this thesis adopts a triangular Discrete Global Grid System (DGGS) as the foundation for a spatiotemporal data management framework. The hierarchical and globally consistent structure of DGGS enables efficient multiresolution representation and indexing of spatial data, while minimizing the geometric distortion that plagues traditional raster grids. Building on this foundation, the proposed framework introduces a tensor-based multiresolution storage scheme coupled with a triangular wavelet scheme defined directly on the DGGS grid. To handle temporal gaps, continuous temporal approximation is achieved using reverse Chaikin subdivision and B-spline curve fitting, allowing the system to not only support smooth approximation and live updates, but also to significantly reduce storage requirements. Together, these components define a scalable framework for real-time multiresolution processing, storage, and retrieval of spatiotemporal raster data. The proposed framework is validated through a case study using data from the RADARSAT Constellation Mission, where experimental results demonstrate efficient encoding, dynamic retrieval, and interactive visualization of time-varying EO data for user-defined regions, while maintaining high data fidelity. Moreover, the framework's scalability and performance are further evaluated using two additional large-scale EO datasets; one spatially extensive dataset covering the entire Canadian region, and another temporally extensive dataset spanning nearly 7 years over Gull Lake, Alberta. 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Existing data management approaches struggle with scalability, geometric distortion, and redundancy. These challenges are further compounded by temporal gaps caused and missing data, and the lack of continuous temporal representations, as well as inefficient support for multiresolution querying and interactive access over arbitrary regions of interest. Together, these limitations hinder the practical use of EO data in time-sensitive and large-scale applications. To address these challenges, this thesis adopts a triangular Discrete Global Grid System (DGGS) as the foundation for a spatiotemporal data management framework. The hierarchical and globally consistent structure of DGGS enables efficient multiresolution representation and indexing of spatial data, while minimizing the geometric distortion that plagues traditional raster grids. Building on this foundation, the proposed framework introduces a tensor-based multiresolution storage scheme coupled with a triangular wavelet scheme defined directly on the DGGS grid. To handle temporal gaps, continuous temporal approximation is achieved using reverse Chaikin subdivision and B-spline curve fitting, allowing the system to not only support smooth approximation and live updates, but also to significantly reduce storage requirements. Together, these components define a scalable framework for real-time multiresolution processing, storage, and retrieval of spatiotemporal raster data. The proposed framework is validated through a case study using data from the RADARSAT Constellation Mission, where experimental results demonstrate efficient encoding, dynamic retrieval, and interactive visualization of time-varying EO data for user-defined regions, while maintaining high data fidelity. Moreover, the framework's scalability and performance are further evaluated using two additional large-scale EO datasets; one spatially extensive dataset covering the entire Canadian region, and another temporally extensive dataset spanning nearly 7 years over Gull Lake, Alberta. The results highlight the framework's ability to handle large volumes of EO data while providing efficient storage and access capabilities."],"dc:identifier.doi":["https://dx.doi.org/10.11575/PRISM/51148"],"dc:identifier.uri":["https://hdl.handle.net/1880/124266"],"dc:language.iso":["en"],"dc:publisher.institution":["University of Calgary"],"dc:rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. 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