| title | GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations |
|---|---|
| authors | Jeonghyeok Do, Munchurl Kim |
| date | 2026-09-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.32510 |
| affiliation | KAIST VIClab (Prof. Munchurl Kim) |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and observation settings. We introduce GeoCR, a generalist model that unifies RGB-only-based CR and multispectral-based CR from single- or multi-temporal cloudy observations, with optional SAR guidance, within a single network. To accommodate different spectral and sensing domains, compact input and output stems extend a pretrained RGB autoencoder while keeping its encoder and decoder trunks frozen. This shared latent interface enables a single flow transformer to jointly model clean RGB and non-RGB latents, conditioned on separate cloudy-observation streams and optional SAR tokens. Through joint pretraining on the training splits of ten datasets comprising 883,331 cloud-free target images, GeoCR learns a shared cloud removal prior across these heterogeneous configurations. The same pretrained checkpoint supports direct inference without dataset-specific fine-tuning and efficient adaptation through low-rank adaptation (LoRA). We evaluate GeoCR against general image restoration and cloud removal methods on test splits of the contributing datasets under full-band and RGB-only settings. GeoCR achieves the best FID and DISTS on full-band SEN12MS-CR and Sen2_MTC_New and RGB-only CUHK-CR2, outperforming existing models and demonstrating the effectiveness of a reusable generative model across diverse settings. |
cite
@misc{do2026geocr,
title={GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations},
author={Jeonghyeok Do and Munchurl Kim},
year={2026},
eprint={2609.32510},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.32510}
}