feat(data): add sampling and transform utilities#2
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March 23, 2026 19:19
Add sampling strategies for training data: - Stratified train/val/test split - Balanced class sampling - Weighted sampler for imbalanced data - K-fold cross-validation splits - Random subset selection for debugging
Add augmentation transforms for satellite data: - Geometric: flip, rotate, compose - Photometric: brightness, contrast, noise - Satellite-specific: cloud masking, normalization - Pre-built training/validation transform pipelines
Goldokpa
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Mar 23, 2026
feat(data): add sampling and transform utilities - Add stratified split, balanced sampling, k-fold CV - Add custom satellite imagery transforms - Add cloud masking and normalization utilities
This was referenced May 17, 2026
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Summary
Changes
src/climatevision/data/sampling.py— Sampling strategies for training datasrc/climatevision/data/transforms.py— Custom transforms for satellite imageryTest plan
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