Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?

2025-01-27

AI Summary

Low spatial resolution, poor cross-regional generalization, and severe tiling artifacts in satellite imagery (e.g., Sentinel-2) hinder downstream geospatial analysis. To address these challenges, we propose a geographically aware super-resolution method. Our key contributions are: (1) the first introduction of learnable geographic location embeddings to explicitly encode regional context and enhance cross-domain generalization; (2) a hybrid framework integrating GAN-based adversarial learning with a diffusion-inspired progressive denoising strategy to improve texture fidelity and structural consistency; and (3) a multi-scale neighborhood feature aggregation module that effectively suppresses tiling boundary artifacts. Evaluated on multi-region benchmarks, our method surpasses state-of-the-art approaches, boosting mIoU by 3.2% on building segmentation. The reconstructed images exhibit high resolution and seamless tiling, demonstrating strong practical potential for urban planning and disaster response applications.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Image Enhancement
• Geospatial Information
• Satellite Imagery

Innovation

• Location-aware Image Enhancement
• Generative Adversarial Networks (GANs)
• Diffusion Models