Refined methodology for large-scale trainings & applications

This release contains the code utilized within the methodology described in the WRI technical note "Spatial Characterization of Urban Land Use through Machine Learning: Mapping Urban Land Use in India and Mexico" (not yet published). Please see release branch README for full project description.

Some distinguishing features of this iteration of the methodology:

• Python 3
• Training imagery downloaded locally; application imagery processed in memory
• Localewise division of training & validation tranches
• Very large training/validation datasets (>10 million samples) made practicable via Keras *_generator functionality
• On-the-fly construction of training/validation/application sets using sample catalog
• Mapping executed locally or on highly parallelized cloud computing infrastructure
• Automated imagery selection for model application
• "Ensemble" final LULC maps derived from multiple model outputs

Initial release of Urban Land Use methodology

This release contains the code utilized within the methodology described in the WRI technical note "Spatial Characterization of Urban Land Use through Machine Learning". Please see release branch README for full project description.

Some distinguishing features of this iteration of the methodology:

• Python 2
• Imagery downloaded locally
• Pixelwise division of training & validation tranches
• Training & validation samples stored and accessed within predefined datasets
• Full training & validation datasets loaded into memory at once for training