Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?

TL;DR

This paper introduces a super-resolution framework for satellite imagery that uses location embeddings and advanced generative models to improve image quality and generalization across regions, addressing tiling artifacts and enhancing urban analysis.

Contribution

The work presents a novel super-resolution method incorporating geographic context via location embeddings and diffusion techniques, improving generalization and image quality in satellite imagery.

Findings

Significant improvement over state-of-the-art super-resolution methods.

Enhanced generalization across diverse geographic regions.

Effective reduction of tiling artifacts in high-resolution outputs.

Abstract

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 Generative Adversarial Networks (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.