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October 20, 2025Open Access

From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis

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The hybrid framework generates geometrically consistent street-view images from satellite imagery.
Utilizing a multi-stage training strategy, the framework shows improved quality in generated street views and fine details.
Evaluation on the CVUSA dataset demonstrates better performance than diffusion-only models and competitive results compared to GAN-based methods.
Integration of Stable Diffusion and PanoGAN enhances the visual fidelity of synthesized images, offering robust representations.

Authors

KBKhawlah BajbaaKing Fahd University of Petroleum and Minerals AAAbbas Anwar MSMuhammad SaqibUniversity of Technology Sydney

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Implication

Model integrates generative adversarial networks and diffusion methods to synthesize street view images from satellite data, suggesting improvements in image quality.

Cite This Study

Bajbaa et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcce8d54a28a75cf1be8 https://doi.org/10.48550/arxiv.2509.24369

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Implication

Model integrates generative adversarial networks and diffusion methods to synthesize street view images from satellite data, suggesting improvements in image quality.

From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis

Key Points

Discussion

Implication

Cite This Study

From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis

Key Points

Discussion

Implication

Cite This Study

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