SENSERO: A Multi-Scale, Multi-Label Sentinel-2 Benchmark for Romania with Cross-Scale Land-Cover Classification Baselines

Patch size is an important design parameter in patch-based Earth observation (EO) learning, yet it is rarely treated as an experimental variable because existing benchmarks use a single fixed scale. We introduce SENSERO, a multi-scale, multi-label Sentinel-2 benchmark for Romania consisting of $10,000$ geographically fixed locations extracted at eight patch sizes, from $64\times 64$ to $336\times 336$ pixels, at a constant 10 m ground sampling distance, so that all scales share identical patch centres and an identical, spatially disjoint $70/15/15$ partition. Each patch provides twelve-band bottom-of-atmosphere reflectance, a rasterised CORINE Land Cover (CLC) 2018 reference map from which five deterministic multi-label annotation layers are derived (three levels of the CLC hierarchy, the BigEarthNet nomenclature, and a scheme adapted to the Romanian landscape). The benchmark task is patch-level multi-label classification; the rasterised reference maps serve to derive and trace the labels, not as a segmentation target. Using ResNet-50 baselines trained from scratch, we find that larger patches bring little benefit at CLC Level 1 but clear gains at Levels 2 and 3 (macro mAP at Level 3 rising from $0.42$ at 64 px to $0.61$ at 336 px), with per-location gains saturating around 224–256 pixels; that transfer across scales is asymmetric, models trained on wide patches transferring to narrow ones far better than the reverse; and that the choice of nomenclature affects measured performance as much as patch size. Leave-scene-out cross-validation, an ablation of the labelling rule, pretrained ResNet-50 and ViT-S/16 controls and bootstrap confidence intervals show that the scale effect is not attributable to spatial leakage, to the annotation rule, or to the baseline architecture. Comparison with an independent 10 m land-cover product shows that the gain from context is concentrated where CLC labels generalise over heterogeneous ground.