Joint Cloud-Resilient Super-Resolution and Restoration Using AlphaEarth Foundation Embeddings
Satellite-based remote sensing is a valuable source of spatial data for monitoring vast territories, yet the availability of high-resolution (HR) imagery remains a major bottleneck. Image super-resolution (SR) offers an alternative to upgrading onboard sensors, but reconstruction quality is often degraded by atmospheric artifacts such as clouds, which are typically handled by pre- or post-processing rather than being integrated into the SR pipeline. In this study, we investigate whether geospatial priors derived from AlphaEarth Foundation (AEF) model embeddings can simultaneously enhance SR reconstruction and facilitate cloud-affected region recovery. We explore both single-image (SISR) and multi-image (MISR) configurations of ESRGAN for RGB and multispectral (MSI) imagery, conditioned on AEF embeddings. For cloud removal, we evaluate Progressive Multi-scale Attention Autoencoder (PMAA) and Conditional Tabular GAN (CTGAN) models, also assessing the effect of AEF conditioning. Using a modified Cloud-S2-NAIP dataset, we demonstrate that AEF conditioning consistently improves SSIM, LPIPS, CLIP-based similarity, and SAM across both SR and cloud-restoration tasks. The AEF-conditioned MISR ESRGAN achieves the best overall LPIPS (0.264) and CLIP similarity (0.952), outperforming both standalone SR configurations and PMAA-/CTGAN-based inpainting. Crucially, we show that AEF conditioning provides an implicit cloud-inpainting effect within the MISR framework, enabling simultaneous spatial enhancement and cloud-damage recovery. This approach offers a promising pipeline for cloud-aware SR in real-world applications such as environmental monitoring and infrastructure analysis.