| title | GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation |
|---|---|
| authors | Jeonghyeok Do, Munchurl Kim |
| date | 2026-09-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.37496 |
| affiliation | KAIST VIClab (Prof. Munchurl Kim) |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Paired synthetic aperture radar (SAR) and electro-optical (EO) imagery is increasingly available across sensors, resolutions, and geographic regions. Yet existing SAR-to-EO image translation (SET) methods are typically trained on a single, limited-scale dataset, producing models specialized to particular sensing conditions. We introduce GeoSET, the first generalist model for SET, built around a single pretrained parent that is adapted to downstream datasets under a common protocol. We curate over 3 million high-quality SAR--EO pairs from a collection of more than 10 million SAR observations, spanning diverse sensors, spatial resolutions, and ground sampling distances. To bridge the modality gap between SAR observations and a pretrained image generator, we develop a speckle-robust SAR encoder and pretrain the conditional generator on this heterogeneous corpus. The resulting parent supports efficient adaptation across downstream datasets through low-rank adaptation (LoRA), updating only 0.60% of the generator parameters and requiring approximately one hour per dataset. Across six downstream benchmarks, GeoSET achieves state-of-the-art results in FID and DISTS with full fine-tuning or LoRA, demonstrating effective transfer across heterogeneous SAR-EO domains. |
cite
@misc{do2026geoset,
title={GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation},
author={Jeonghyeok Do and Munchurl Kim},
year={2026},
eprint={2609.37496},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.37496}
}