Can DGGS provide an AI-ready spatial framework that eliminates the need for costly harmonization?

Anne Fouilloux

2026-01-25T09:24:13.269Z

DGGS as an AI-Ready Framework for Multi-Source Earth Observation Data Integration

Anne Fouilloux

2026-01-25T09:24:13.269Z

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

DGGS-based spatial indexing as a harmonization framework

Anne Fouilloux

2026-01-25T09:24:13.269Z

Anne Fouilloux

2026-01-25T09:24:13.269Z

Multi-source EO datasets requiring integration for AI/ML applications

Anne Fouilloux

2026-01-25T09:24:13.269Z

Traditional harmonization workflows (reprojection, resampling, vector-raster conversion)

Anne Fouilloux

2026-01-25T09:24:13.269Z

Preprocessing time/cost, data alignment accuracy, AI model performance, reproducibility across research groups

Anne Fouilloux

2026-01-25T09:24:13.269Z

What quantum computing and quantum-inspired approaches have been applied or proposed for biodiversity research and conservation, and what evidence exists for their computational advantages over classical methods?

Anne Fouilloux

2025-12-24T17:06:38.000Z

Quantum Computing Applications for Biodiversity Research and Conservation: A Scoping Review

Anne Fouilloux

2025-12-24T17:06:38.000Z