Sentinel-2-specific foundation models outperform ImageNet on multi-continent crop mapping, with 100 labels achieving high overall accuracy but 900 required to address class imbalance.
Bridging optical and sar satellite image time series via contrastive feature extraction for crop classification,
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On the Generalizability of Foundation Models for Crop Type Mapping
Sentinel-2-specific foundation models outperform ImageNet on multi-continent crop mapping, with 100 labels achieving high overall accuracy but 900 required to address class imbalance.