JohnNay/forecastVeg
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Automates MODIS satellite data ingestion and preprocessing, then trains gradient-boosted models (via H2O) on hundreds of millions of spatiotemporal pixel observations to predict Enhanced Vegetation Index across multi-year timescales. Uses spatially-stratified train/test splitting and nested hyperparameter search via Hyperopt to compare raw spectral bands against Level-3 MODIS products, requiring 100+ GB RAM and multi-threaded compute for production-scale training.
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Python
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Feb 22, 2021
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