Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?

Daniel Panangian, Ksenia Bittner; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6136-6145

Publicly available satellite imagery such as Sentinel-2 often lacks the spatial resolution required for accurate analysis of remote sensing tasks including urban planning and disaster response. Current super-resolution techniques are typically trained on limited datasets leading to poor generalization across diverse geographic regions. In this work we propose a novel super-resolution framework that enhances generalization by incorporating geographic context through location embeddings. Our framework employs GANs and incorporates techniques from diffusion models to enhance image quality. Furthermore we address tiling artifacts by integrating information from neighboring images enabling the generation of seamless high-resolution outputs. We demonstrate the effectiveness of our method on the building segmentation task showing significant improvements over state-of-the-art methods and highlighting its potential for real-world applications.