When happened: March - June 2022
Advancing a framework to complement cognitive analysis / interpretation with ML methods to support regional to national scale geological mapping.
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Leads / Advisors
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Hazen Russell | Lead
Sedimentologist GS
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William Parkinson | Lead
Technical Product Manager, EarthDaily Analytics
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~Nicolas Benoit | Lead
Hydrogeologist, GSC
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Overview
- Challenge - mapping geological materials that can have overlapping spectral signals and landscape positions. Extraction of irregular landform shapes and sizes that have inconsistent composition, moisture regime, and vegetation.
- Tools - Machine Learning in Python using scikit-learn, TensorFlow or PyTorch algorithms (classification, regression, clustering, Convolutional Neural Network and more) and Geostatistic (spatial covariance, interpolation, simulation, scaling and uncertainty characterization).
- The goal is to build an understanding of the challenge of replacing abstract cognitive analysis with ML analysis.
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Top Contributors
~ Winning $2500 in prizes for notable progress
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A bit about our experience
Details
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