SOCrebuttal — Soil Organic Carbon Mapping (Geoderma rebuttal artifacts)

Architecture comparison, production maps, and supporting analyses for Bavaria-wide soil organic carbon mapping at LUCAS data scale (~16,360 samples). Published as the artifact bundle accompanying the manuscript revision.

Headline finding

A lightweight (~215k-parameter) CNN + Transformer hybrid is the recommended architecture at this data scale (Vanilla in the comparison table):

• +0.23 R² gain from the CNN spatial encoder over a parameter-matched transformer-alone baseline (Lightweight).
• 4× lower cross-fold variance than transformer-alone.
• ~3× faster convergence (median best-epoch 7-8 vs 20).
• No measurable benefit from the gated-residual mechanism on top of CNN+Transformer (SGT and Vanilla tie at matched hyperparameters; SGT is 1.7× larger for no R² gain).
• 30× larger pure-transformer (SimpleTransformerV2, 11M params) matches Vanilla in mean R² but offers no other advantages.

See final_models/maps_comparison_2023.png for the cross-architecture production-map figure, and final_models/maps_comparison_2023_rebal.png for the KDE-rebalanced training variant.

Repository structure

• sweep/ — Spatial-CV k-fold sweep RESULTS (per-config kfold_results_summary.json, per-fold metrics, sweep_ranking, README). Excludes the heavy .pth files — request "sweep-checkpoints" for those.

Methodology summary

• Spatial cross-validation: 10-fold latitude-decile splits with a 1.2 km train/test buffer (Roberts 2017, Ploton 2020). The R² values reported in sweep/sweep_ranking.md are the honest generalization estimates.
• Production maps: full-data training (95% train + 5% random monitor holdout — non-spatial, used only for best-epoch weight selection). All architectures inferred on the 1mil-point Bavaria reference grid for target year 2023 over a 5-year covariate window {2019, …, 2023}.
• Rebalanced production maps (*_rebal): same architectures, retrained with KDE-inverse-density sample weighting on log(SOC) at α=0.5 (Yang et al. ICML 2021) to ensure organic-rich regions (Alpine peat, fen/bog) are not under-predicted.

Reproducing

All training, inference, and analysis scripts are in code/. Architecture source under code/architectures/; rebuttal pipeline scripts (run_kfold, train_full, infer_bavaria, sweep_submit, submit_finals, inspect_run, compare_maps) under code/rebuttal/.

Citation

Will be populated once the manuscript is accepted.

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Last updated: fourel1@jpbl-s01-03