QDANN (reimplementation)

A clean-room reimplementation of the Quantile-loss Domain Adversarial Neural Network from:

Ma, Y., Liang, S.-Z., Myers, D.B., Swatantran, A., Lobell, D.B. (2024). Subfield-level crop yield mapping without ground truth data: A scale transfer framework. Remote Sensing of Environment 315, 114427. https://doi.org/10.1016/j.rse.2024.114427

Not affiliated with or endorsed by the authors. The published model code was never released; this is written from the paper alone. The authors' yield maps are distributed separately under CC-BY-NC-SA 4.0 and are not used as an input here, this implementation trains only on public-domain data (USDA NASS Quick Stats, Landsat, gridMET, USDA CDL), so its outputs carry no non-commercial restriction.

Status

Model implementation. No feature pipeline, no trained weights, no yield maps yet.

Layout

Path	Contents
SPEC.md	Every equation and architectural claim in the paper, transcribed to pseudocode with section references
AMBIGUITIES.md	Details the paper leaves unspecified, what was assumed, and expected impact
config.toml	Hyperparameters, each tagged paper_specified / reasonable_assumption / implementation_choice
src/qdann/	Model, losses, synthetic-data check
tests/	Shape, gradient-sign, and loss-asymmetry checks

Development

uv sync
uv run pytest -v

Torch is pinned to the CPU build (torch==2.9.1+cpu) via an explicit index — the model is ~50k parameters and never needs a GPU.