WIP: Add spatial-temporal metadata embeddings to CropModel

[bw4sz]

Summary

• Adds optional late-fusion spatial-temporal metadata (lat, lon, date) to CropModel for improved species classification
• Sinusoidal encoding of coordinates + day-of-year through a small MLP (~1.5% of image feature vector), concatenated with ResNet-50 features before the classifier
• Fully backward compatible — metadata is optional at both training and inference time; model gracefully degrades to image-only predictions when metadata is absent

Key changes

• SpatialTemporalEncoder: Sinusoidal positional encoding + MLP (6 → configurable dim, default 32)
• CropModel: Splits into backbone + metadata encoder + classifier when use_metadata=True
• MetadataImageFolder: Wraps ImageFolder with a CSV sidecar (filename,lat,lon,date) for training
• BoundingBoxDataset: Supports per-crop metadata dict for inference
• predict_tile: Accepts metadata={"lat": ..., "lon": ..., "date": "YYYY-MM-DD"}
• Config: use_metadata, metadata_dim, metadata_dropout
• 19 new tests covering encoder, model, datasets, checkpoint save/load, and full training integration
• Documentation added to CropModel user guide and configuration reference

Test plan

• All 19 new metadata tests pass (pytest tests/test_metadata_cropmodel.py)
• All 8 existing crop model tests pass (pytest tests/test_crop_model.py)
• Full test suite passes on CI
• Manual smoke test with real field data

🤖 Generated with Claude Code

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Note

Medium Risk
Touches core CropModel training/inference data flow and changes batch/forward signatures to support metadata; defaults remain unchanged, but incorrect metadata wiring or shape handling could break crop-model prediction/training in edge cases.

Overview
Adds optional spatial-temporal metadata (lat/lon/date) support to CropModel, including a new SpatialTemporalEncoder and a backbone+metadata+classifier path when cropmodel.use_metadata is enabled, while keeping image-only behavior as the default.

Training can now load per-image metadata from a sidecar CSV via CropModel.load_from_disk(..., metadata_csv=...), and inference can pass image-level metadata through deepforest.predict_tile(..., metadata=...) which is expanded to per-crop tensors and threaded through predict/BoundingBoxDataset.

Updates config/schema/docs to expose use_metadata, metadata_dim, and metadata_dropout, and adds a dedicated test suite covering encoder behavior, training/predict steps, dataset wrappers, and checkpoint save/load.

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