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.
SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets]
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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.