GLOBE Curation AI

GLOBE Curation AI

This project unlocks over three decades of crowdsourced NASA GLOBE water transparency data for rigorous environmental and hydrological modeling. While field collection using manual tools, such as turbidity tubes and Secchi disks, inherently introduces observer variance and calibration noise, our custom AI architecture automatically resolves these data integrity bottlenecks. The pipeline deploys a Bayesian hierarchical engine with split likelihoods to isolate environmental anomalies and account for physical instrument limits, paired with Sentinel-2 satellite spatial validation to cross-verify geographic accuracy. The outcome is a continuously validated open dataset optimized for scientific analysis, now paired with an edge-deployed AI assistant that provides real-time guidance to field collaborators to eliminate collection errors at the source.

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  • Organization
    Harvard University and NASA
  • Profession
    AI system improving crowdsourced water transparency data quality

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