Satellite-to-Street: Synthesizing Post-Disaster Views from Satellite Imagery via Generative Vision Models
In the immediate aftermath of natural disasters, rapid situational awareness is critical. Traditionally, satellite observations are widely used to estimate damage extent. However, they lack the ground-level perspective essential for characterizing specific structural failures and impacts. Meanwhile, ground-level data (e.g., street-view imagery) remains largely inaccessible during time-sensitive events. This study investigates Satellite-to-Street View Synthesis to bridge this data gap. We introduce two generative strategies to synthesize post-disaster street views from satellite imagery: a Vision-Language Model (VLM)-guided approach and a damage-sensitive Mixture-of-Experts (MoE) method. W...
| Authors | Yifan Yang, Lei Zou, Wendy Jepson |
|---|---|
| Primary category | cs.CV |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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