用物理约束与地形信息生成更真实的洪水卫星图像。
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
- 融合浅水方程物理约束与多尺度地形特征，提升洪水区域结构合理性。
- 在真实洪水数据集上，生成图像与真实影像匹配度更高，洪水区域一致性更强。
- 适合遥感、灾害模拟与数据增强领域的研究者使用。
构建可靠的洪水评估模型需要高质量的灾前灾后卫星图像配对数据，但这类数据仍稀缺。尽管生成模型可作为数据增强手段，现有方法常导致洪水区域布局不自然、场景结构失真。我们提出 FlowForm 框架，结合受浅水方程启发的潜在空间正则化与结构感知条件输入。洪水描述模块（FDM）在扩散瓶颈处的辅助潜在空间中对稳态浅水方程残差施加可微分惩罚；地形锚点适配器（TAA）在 U-Net 编码器的四个尺度注入深度、语义与边缘特征。我们还构建了 FloodScape 大规模高分辨率数据集，包含灾前灾后成对卫星图像。除标准图像生成指标外，还评估洪水区域一致性、零样本泛化至地理隔离洪水事件的能力，以及各组件敏感性。所有对比实验中，FlowForm 在视觉保真度、配对图像相似性及洪水区域一致性方面均表现更优。
原文摘要 · Abstract (English)
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.
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