用卫星图生成灾后街景，帮救援人员看清实际损毁情况。
Satellite-to-Street: Synthesizing Post-Disaster Views from Satellite Imagery via Generative Vision Models
- 用视觉语言模型和损伤敏感的专家混合模型，从卫星图生成街景。
- 控制网模型语义准确率最高（0.71），但易虚构结构细节。
- 提出新评估框架，兼顾像素质量、语义一致性和视觉感知匹配。
灾后快速获取真实现场信息至关重要。传统卫星图像虽能评估破坏范围，却缺乏地面视角，难以识别具体结构损伤；而地面影像在紧急情况下往往无法获取。本文研究卫星到街景的合成技术，提出两种生成策略：基于视觉语言模型（VLM）引导的方法和损伤敏感的专家混合（MoE）方法。在300个灾情场景上对比通用基线（Pix2Pix、ControlNet），采用多层级评估框架：（1）像素级质量评估，（2）基于ResNet的语义一致性验证，（3）新型VLM作为评判者进行感知对齐评估。实验显示存在真实感与保真度的权衡：扩散模型（如ControlNet）虽具高感知真实感，但常产生虚构结构；标准ControlNet在语义准确率上达0.71，表现最佳；而增强型VLM与MoE模型在纹理合理性上更优，但语义清晰度不足。本工作为可信跨视角合成建立基准，强调视觉逼真未必代表结构信息可靠，对灾后评估有重要警示意义。
原文摘要 · Abstract (English)
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. We benchmark these against general-purpose baselines (Pix2Pix, ControlNet) using a proposed Structure-Aware Evaluation Framework. This multi-tier protocol integrates (1) pixel-level quality assessment, (2) ResNet-based semantic consistency verification, and (3) a novel VLM-as-a-Judge for perceptual alignment. Experiments on 300 disaster scenarios reveal a critical realism--fidelity trade-off: while diffusion-based approaches (e.g., ControlNet) achieve high perceptual realism, they often hallucinate structural details. Quantitative results show that standard ControlNet achieves the highest semantic accuracy, 0.71, whereas VLM-enhanced and MoE models excel in textural plausibility but struggle with semantic clarity. This work establishes a baseline for trustworthy cross-view synthesis, emphasizing that visually realistic generations may still fail to preserve critical structural information required for reliable disaster assessment.
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