A seven-year Swiss crop mapping benchmark with a leave-one-year-out protocol reveals that a spatio-temporal transformer (TSViT) beats a convolutional temporal-attention model (U-TAE) by 12 points in macro-mIoU, while an Earth observation foundation model (Galileo) trails both.
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SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping
A seven-year Swiss crop mapping benchmark with a leave-one-year-out protocol reveals that a spatio-temporal transformer (TSViT) beats a convolutional temporal-attention model (U-TAE) by 12 points in macro-mIoU, while an Earth observation foundation model (Galileo) trails both.