FlowForm：融合流体物理与拓扑一致性的卫星洪水合成方法
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
AI总结：
FlowForm是融合流体物理与拓扑一致性的卫星洪水合成框架，通过FDM、TAA及FloodScape数据集，在洪水图像生成的视觉保真度、配对图像相似性等指标上优于现有方法。
AI中文摘要：
开发稳健的洪水评估模型需要高质量的配对卫星图像，但针对洪水的特定图像生成数据仍然稀缺。尽管生成模型为数据增强提供了有前景的手段，现有方法却常产生不合理的淹没区域空间布局并扭曲场景结构。我们提出FlowForm，一种用于卫星洪水合成的框架，它将基于SWE的潜在正则化与结构感知条件相结合。洪水描述符模块（FDM）在扩散瓶颈处的辅助潜在场中对稳态浅水方程（Shallow Water Equation）的残差施加可微惩罚。地形锚定适配器（TAA）在U-Net的四个编码器尺度注入深度、语义和边缘特征。我们还整理了FloodScape，一个包含灾害前后配对卫星图像的大规模高分辨率数据集。除了标准图像生成指标，我们还评估了淹没区域的一致性、对地理上未见过的洪水事件的零样本泛化能力以及对各组件的敏感性。在所有报告的对比中，FlowForm实现了更高的视觉保真度、配对图像间更强的相似性以及更优的淹没区域一致性。
英文摘要：
Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded regions and distort scene structures. We propose FlowForm, a framework for satellite flood synthesis that integrates SWE-inspired latent regularization with structure-aware conditioning. The Flood Descriptor Module (FDM) imposes differentiable penalties on residuals of the steady-state Shallow Water Equation in auxiliary latent fields at the diffusion bottleneck. The Terrain Anchor Adapter (TAA) injects depth, semantic, and edge features at four encoder scales of the U-Net. We further curate FloodScape, a large-scale, high-resolution dataset comprising paired satellite images acquired before and after disasters. In addition to standard image-generation metrics, we evaluate the consistency of flooded regions, zero-shot generalization to a geographically held-out flood event, and sensitivity to individual components. Across all reported comparisons, FlowForm achieves higher visual fidelity, greater similarity between paired images, and stronger consistency of flooded regions.
