sub:assertion {
  <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/research-question> dct:audience "Multi-source EO datasets requiring integration for AI/ML applications" ;
    dct:description "Can DGGS provide an AI-ready spatial framework that eliminates the need for costly harmonization?" ;
    dct:relation "Traditional harmonization workflows (reprojection, resampling, vector-raster conversion)" ;
    dct:subject "DGGS-based spatial indexing as a harmonization framework" ;
    dct:title "DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration" ;
    dct:type <https://w3id.org/np/RAfZfE1gbUtc35W7xT12XTO0ptZwycN2-jj7Jow6COAoQ/effectiveness> ;
    <http://schema.org/expectedResult> "Preprocessing time/cost, data alignment accuracy, AI model performance, reproducibility across research groups" ;
    rdfs:comment "Multi-source Earth observation data cannot be directly fed to AI algorithms without costly spatial harmonization — including reprojection, resampling, and vector-raster conversion. This preprocessing bottleneck limits the scalability and reproducibility of machine learning workflows in EO. DGGS offers a potential solution by providing a standardized spatial index where heterogeneous datasets become directly associable via zone IDs, potentially eliminating traditional harmonization steps. However, no systematic synthesis exists evaluating DGGS effectiveness specifically for AI-ready data preparation. This review will assess whether DGGS can serve as a scalable, interoperable framework that enables direct ingestion of multi-source EO data into AI pipelines." .
}