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LALE achieves near state-of-the-art performance in land-cover segmentation while using 4.5x fewer parameters and 17x fewer GMACs than leading models.
Synthetic data, when grounded in vision-language models for evaluation, demonstrably boosts performance in remote sensing tasks like segmentation and captioning, outperforming models trained solely on real-world data.