. . . . "Multi-source EO datasets requiring integration for AI/ML applications" . "Can DGGS provide an AI-ready spatial framework that eliminates the need for costly harmonization?" . "Traditional harmonization workflows (reprojection, resampling, vector-raster conversion)" . "DGGS-based spatial indexing as a harmonization framework" . "DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration" . . "Preprocessing time/cost, data alignment accuracy, AI model performance, reproducibility across research groups" . "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." . . "Anne Fouilloux" . "2026-01-25T09:24:13.269Z"^^ . . . . "PICO Research Question: DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration" . . . . . "RSA" . "MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDWv2pJnmDsBOq8OlT1aSvYXSuWT34WOp4FYqEzdnn2F0kqzcFevBqWGZDxJWC0lqCrDEuNfp2QFyPe/+nES9dlHGYIhqPi68fwK6ZiNUotRFxXou+rjFznVvUxtCL8Ede79EBHwWN61QtwSIcU12bLoZsNPFlqQASQ93BJuKlihwIDAQAB" . "tkFXjfc0VSB8gfvQ7a30qSMaHyAvJU5d/CloirG8k4gfDCvDPxQC7Tw4iTp6oB6ZRcp2X9y6hKD1HSzb9FyEdRnJgDIbzNxRKfjQNtd3XocEkAZE4fkgXttfNqa1xKtRFoJUhaBUPBUSAvmR/tLQGVvw/KrOQYpMY0LnDoxLOcQ=" . . .