Auditing GeoFM Evaluation for Field-Extent Segmentation: Label Proxies, Baselines, and When Frozen Features Match Fine-tuning
Geospatial foundation-model (GeoFM) benchmarks for agricultural field-extent segmentation often rely on cropland land-cover proxies and compare against per-pixel spectral baselines. We measure how these choices affect conclusions using identical single-date Sentinel-2 pixels from six countries. Replacing polygon-derived field labels with ESA WorldCover cropland raises random-forest AUROC from 0.55-0.82 to 0.79-0.96, showing that the proxy creates a more spectrally separable task. On the actual field-extent labels, a from-scratch U-Net reaches 0.89-0.98 AUROC and has higher point AUROC than a Prithvi frozen linear probe in five of six regions. We then compare a frozen backbone and full fine-tuning using the same decoder for Prithvi and TerraMind, with ten matched seeds. At $\varepsilon=0.02$ AUROC, 9 of 12 region$\times$model cells are TOST-equivalent. Kenya is the common failure case: positives cover roughly 0.1% of pixels, and full fine-tuning performs better for both models. Cross-region results are less favorable to frozen models. On AUROC and average precision (AP), the U-Net leads the full 30-transfer matrix, while an exploratory analysis excluding Kenya finds that both frozen decoders outperform their fine-tuned counterparts; no AUROC difference is detected between frozen TerraMind and the U-Net in that subset. For Prithvi, a frozen decoder trains about $110\times$ fewer parameters than full fine-tuning. These results argue for auditing labels and baselines before interpreting GeoFM rankings, and for testing a frozen decoder before assuming that full fine-tuning will help. Our conclusions are limited to single-date, per-pixel field-extent segmentation, not parcel-boundary or instance delineation.