AI RESEARCH
Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models
arXiv CS.LG
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ArXi:2605.08521v1 Announce Type: cross 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.