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GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation

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Briefings

GeoSET is the first generalist foundation model for SAR-to-EO image translation, built on a single pretrained parent adapted via low-rank adaptation. It curates over 3 million high-quality SAR-EO pairs from over 10 million observations, enabling state-of-the-art performance across six downstream benchmarks with minimal parameter updates.

Key points

• GeoSET introduces a single pretrained parent model adapted via low-rank adaptation (LoRA) to diverse SAR-EO datasets.
• The model achieves state-of-the-art results in FID and DISTS metrics across six downstream benchmarks.

Context

Existing SAR-to-EO translation methods are limited to single-scale datasets, requiring model-specific training. GeoSET addresses this by curating over 3 million high-quality pairs from 10+ million observations, enabling efficient cross-domain adaptation.

Limitations

Evidence covers only six downstream benchmarks; results are presented as state-of-the-art without statistical significance testing. Preprint status and uncertainty regarding generalizability across all sensor types remain unverified.

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