用单目航拍图+地形图估算洪水深度，无需复杂模拟。
Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models
- 用Transformer分割模型生成洪水范围，再结合地形图计算水位高程。
- 在两个数据集上验证，能准确估算每像素洪水深度。
- 适合灾后快速评估、城市洪涝研究者使用。
灾后态势感知依赖于对洪水范围和体积的准确理解。尽管2D语义分割可精确识别洪水区域，但缺乏垂直维度信息，难以评估通行性与结构风险。本文提出一种基于几何的“水面高程”方法，仅通过单目航拍图像估算洪水深度。该流程利用当前最优的Transformer分割模型Mask2Former生成精确的2D洪水掩码，并与数字高程模型（DEM）融合，识别水陆边界，计算全局水面高程（Z_water），依据局部流体静力学原理推算每像素洪水深度。我们在FloodNet和CRASAR-U-DROIDS数据集上验证该流程，证明高性能分割可有效从2D影像中提取3D体积数据，且避免了水动力模拟带来的延迟。
原文摘要 · Abstract (English)
Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters. While 2D semantic segmentation provides accurate flood masking, it lacks the vertical dimension required to assess navigability and structural risk. This paper presents a geometric "Water Surface Elevation" approach for estimating flood depth from monocular aerial imagery. Our pipeline utilizes Mask2Former, a state-of-the-art transformer-based segmentation model, to generate precise 2D flood masks. These masks are fused with Digital Elevation Models (DEMs) to identify the water-land boundary, calculate a global water surface elevation ($Z_{water}$), and compute per-pixel depth based on the principle of local hydrostatic equilibrium. We evaluate this workflow using the FloodNet and CRASAR-U-DROIDS datasets, demonstrating how high-performance segmentation can be leveraged to extract 3D volumetric data from 2D imagery without the latency of hydrodynamic simulations.
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