HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation

TL;DR

HuiYanEarth-SAR is a foundational model that generates high-fidelity global SAR imagery from geographic coordinates, integrating geospatial priors and scattering mechanisms to enhance realism and utility.

Contribution

This work introduces the first foundational SAR generation model combining geospatial priors with scattering mechanism modeling for global high-fidelity image synthesis.

Findings

Successfully generates realistic SAR images globally based on coordinates.

Bridges geography, scattering mechanisms, and AI for SAR simulation.

Advances SAR research from perception to simulation and creation.

Abstract

Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamentally alleviating the data scarcity bottleneck that constrains development in this field. However, existing methods find it difficult to simultaneously ensure high fidelity in both global geospatial semantics and microscopic scattering mechanisms, resulting in severe challenges for global generation. To address this, we propose \textbf{HuiYanEarth-SAR}, the first foundational SAR imagery generation model based on AlphaEarth and integrated scattering mechanisms. By injecting geospatial priors to control macroscopic structures and utilizing implicit scattering characteristic modeling to ensure the authenticity of microscopic textures, we achieve the capability of generating high-fidelity SAR images for global locations solely based on geographic coordinates. This study not only constructs an efficient SAR scene simulator but also establishes a bridge connecting geography, scatter mechanism, and artificial intelligence from a methodological standpoint. It advances SAR research by expanding the paradigm from perception and understanding to simulation and creation, providing key technical support for constructing a high-confidence digital twin of the Earth.