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Towards universal building blocks for cloud-native digital-twins

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Title: Towards universal building blocks for cloud-native digital-twins
Authors: Kmoch, Alexander, Chan, Wai, Tik, Ameline, Guillaume, Magin, Justus, Delouis, Jean-Marc, Odaka, Tina, Bovy, Benoit, Fouilloux, Anne, Uuemaa, Evelyn
Contributors: University of Salzburg, Tartu University Hospital, The Chinese University of Hong Kong (CUHK), Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO EPE)-Centre National de la Recherche Scientifique (CNRS), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Simula Research Laboratory OSLO, Tomko M., Ivanova I.
Source: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ; 2025 Free and Open Source Software for Geospatial, FOSS4G 2025 ; https://univ-brest.hal.science/hal-05630546 ; 2025 Free and Open Source Software for Geospatial, FOSS4G 2025, Nov 2025, Auckland, New Zealand. pp.45 - 52, ⟨10.5194/isprs-archives-xlviii-4-w20-2025-45-2026⟩
Publisher Information: CCSD
International Society for Photogrammetry and Remote Sensing
Publication Year: 2025
Collection: Université de Bretagne Occidentale: HAL
Subject Terms: web service, DGGS, OGC API, Zarr, Parquet, data cube, Large datasets, Application programming interfaces (API), Data handling, Digital storage, Ecosystems, Grid computing, Indexing (of information), Information management, Interoperability, Websites, [SDE]Environmental Sciences
Subject Geographic: Auckland, New Zealand
Description: International audience ; The exponential growth of Earth Observation (EO) data presents significant challenges for efficient data access, processing, and analysis. Current approaches often involve disparate data formats, coordinate systems, and access patterns, limiting interoperability and scalability. Firstly, the Zarr and Parquet data storage formats have seen wide adoption as a unifying cloud-native foundation for various domains in recent years, including climate, EO, bio-imaging, and genomics. Secondly, Discrete Global Grid Systems (DGGS) such as HEALPix, or ISEA-based hexagonal DGGS are being increasingly used to enable spatial data indexing beyond traditional grids, by using equal-area pixels and location-and refinement level encoding indices. Lastly, the recently published OGC API DGGS standard specifies a lightweight web service API for clients accessing data organised according to Discrete Global Grid Reference Systems (DGGRS). We describe a scalable, interoperable, and extensible FOSS architecture for a modern geospatial data ecosystem, based on DGGS. As an example, we introduce pydggsapi, a Python server implementing this new OGC standard to serve large geospatial datasets from cloud-native Zarr and Parquet data stores indexed by a DGGS. This novel architecture combines the DGGS data cube paradigm, standardised OGC API DGGS web service access, and cloud-optimised data formats such as Zarr and Parquet as universal building blocks for geospatial data management, enabling seamless transitions between high-performance computing environments and lightweight client applications.
Document Type: conference object
Language: English
DOI: 10.5194/isprs-archives-xlviii-4-w20-2025-45-2026
Availability: https://univ-brest.hal.science/hal-05630546
https://univ-brest.hal.science/hal-05630546v1/document
https://univ-brest.hal.science/hal-05630546v1/file/isprs-archives-XLVIII-4-W20-2025-45-2026.pdf
https://doi.org/10.5194/isprs-archives-xlviii-4-w20-2025-45-2026
Rights: https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.5066BE82
Database: BASE
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  Data: Towards universal building blocks for cloud-native digital-twins
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  Data: <searchLink fieldCode="AR" term="%22Kmoch%2C+Alexander%22">Kmoch, Alexander</searchLink><br /><searchLink fieldCode="AR" term="%22Chan%2C+Wai%2C+Tik%22">Chan, Wai, Tik</searchLink><br /><searchLink fieldCode="AR" term="%22Ameline%2C+Guillaume%22">Ameline, Guillaume</searchLink><br /><searchLink fieldCode="AR" term="%22Magin%2C+Justus%22">Magin, Justus</searchLink><br /><searchLink fieldCode="AR" term="%22Delouis%2C+Jean-Marc%22">Delouis, Jean-Marc</searchLink><br /><searchLink fieldCode="AR" term="%22Odaka%2C+Tina%22">Odaka, Tina</searchLink><br /><searchLink fieldCode="AR" term="%22Bovy%2C+Benoit%22">Bovy, Benoit</searchLink><br /><searchLink fieldCode="AR" term="%22Fouilloux%2C+Anne%22">Fouilloux, Anne</searchLink><br /><searchLink fieldCode="AR" term="%22Uuemaa%2C+Evelyn%22">Uuemaa, Evelyn</searchLink>
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  Data: University of Salzburg<br />Tartu University Hospital<br />The Chinese University of Hong Kong (CUHK)<br />Laboratoire d'Océanographie Physique et Spatiale (LOPS)<br />Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO EPE)-Centre National de la Recherche Scientifique (CNRS)<br />German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ)<br />Simula Research Laboratory OSLO<br />Tomko M.<br />Ivanova I.
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  Data: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives ; 2025 Free and Open Source Software for Geospatial, FOSS4G 2025 ; https://univ-brest.hal.science/hal-05630546 ; 2025 Free and Open Source Software for Geospatial, FOSS4G 2025, Nov 2025, Auckland, New Zealand. pp.45 - 52, ⟨10.5194/isprs-archives-xlviii-4-w20-2025-45-2026⟩
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  Data: 2025
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  Data: International audience ; The exponential growth of Earth Observation (EO) data presents significant challenges for efficient data access, processing, and analysis. Current approaches often involve disparate data formats, coordinate systems, and access patterns, limiting interoperability and scalability. Firstly, the Zarr and Parquet data storage formats have seen wide adoption as a unifying cloud-native foundation for various domains in recent years, including climate, EO, bio-imaging, and genomics. Secondly, Discrete Global Grid Systems (DGGS) such as HEALPix, or ISEA-based hexagonal DGGS are being increasingly used to enable spatial data indexing beyond traditional grids, by using equal-area pixels and location-and refinement level encoding indices. Lastly, the recently published OGC API DGGS standard specifies a lightweight web service API for clients accessing data organised according to Discrete Global Grid Reference Systems (DGGRS). We describe a scalable, interoperable, and extensible FOSS architecture for a modern geospatial data ecosystem, based on DGGS. As an example, we introduce pydggsapi, a Python server implementing this new OGC standard to serve large geospatial datasets from cloud-native Zarr and Parquet data stores indexed by a DGGS. This novel architecture combines the DGGS data cube paradigm, standardised OGC API DGGS web service access, and cloud-optimised data formats such as Zarr and Parquet as universal building blocks for geospatial data management, enabling seamless transitions between high-performance computing environments and lightweight client applications.
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  Data: 10.5194/isprs-archives-xlviii-4-w20-2025-45-2026
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  Data: https://univ-brest.hal.science/hal-05630546<br />https://univ-brest.hal.science/hal-05630546v1/document<br />https://univ-brest.hal.science/hal-05630546v1/file/isprs-archives-XLVIII-4-W20-2025-45-2026.pdf<br />https://doi.org/10.5194/isprs-archives-xlviii-4-w20-2025-45-2026
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