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@ARTICLE{Krber:1047696,
      author       = {Kröber, Felix and Sudmanns, Martin and Abad, Lorena and
                      Tiede, Dirk},
      title        = {{O}n-demand, semantic {EO} data cubes – knowledge-based,
                      semantic querying of multimodal data for mesoscale analyses
                      anywhere on {E}arth},
      journal      = {ISPRS journal of photogrammetry and remote sensing},
      volume       = {228},
      issn         = {0924-2716},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2025-04462},
      pages        = {552 - 565},
      year         = {2025},
      abstract     = {With the daily increasing amount of available Earth
                      Observation (EO) data, the importance of processing
                      frameworks that allow users to focus on the actual analysis
                      of the data instead of the technical and conceptual
                      complexity of data access and integration is growing. In
                      this context, we present a Python-based implementation of
                      ad-hoc data cubes to perform big EO data analysis in a few
                      lines of code. In contrast to existing data cube frameworks,
                      our semantic, knowledge-based approach enables data to be
                      processed beyond its simple numerical representation, with
                      structured integration and communication of expert knowledge
                      from the relevant domains. The technical foundations for
                      this are threefold: Firstly, on-demand fetching of data in
                      cloud-optimized formats via SpatioTemporal Asset Catalog
                      (STAC) standardized metadata to regularized
                      three-dimensional data cubes. Secondly, provision of a
                      semantic language along with an analysis structure that
                      enables to address data and create knowledge-based models.
                      And thirdly, chunking and parallelization mechanisms to
                      execute the created models in a scalable and efficient
                      manner. From the user’s point of view, big EO data
                      archives can be analyzed both on local, commercially
                      available devices and on cloud-based processing
                      infrastructures without being tied to a specific platform.
                      Visualization options for models enable effective exchange
                      with end users and domain experts regarding the design of
                      analyses. The concrete benefits of the presented framework
                      are demonstrated using two application examples relevant for
                      environmental monitoring: querying cloud-free data and
                      analyzing the extent of forest disturbance areas.},
      cin          = {IBG-2},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.isprsjprs.2025.07.015},
      url          = {https://juser.fz-juelich.de/record/1047696},
}