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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." .
}

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  orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .
  
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    dct:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:wasCreatedAt <https://nanodash.knowledgepixels.com/>;
    rdfs:label "PICO Research Question: DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration";
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