Unsupervised Estimation of Post-Event Standing Urban Floodwater Depth Using Aerial Imagery and Digital Terrain Models

Abstract

Accurate estimation of floodwater depth is vital for disaster management but traditionally relies on data-intensive hydrodynamic models or supervised deep learning restricted by labeled data requirements. To address these bottlenecks, this study proposes a fully unsupervised, training-free framework for rapid depth estimation of standing or slowly receding residual floodwater using post-event remote sensing imagery and DTMs. First, a binary flood extent map is automatically delineated by adapting an existing unsupervised color-based segmentation algorithm for UAV imagery. Second, leveraging the hydrostatic equilibrium principle, floodwater depth is computed by integrating the extracted flood footprint with the underlying DTM. This framework was evaluated using the Inundation2Depth dataset, encompassing twelve urban and peri-urban sites in the Southeastern US impacted by Hurricanes Matthew and Florence. Experimental results across all examined sites demonstrated the framework’s viability, with segmentation F1-scores ranging from 63% to 96% and absolute flood depth RMSE ranged from 0.16 m in well-defined catchments to 1.69 m in highly obscured regions. Bypassing the need for manual annotations and task-specific training, the proposed framework offers a scalable, rapidly deployable solution for first-order flood mapping and depth estimation. Its computational efficiency enables execution on standard CPU hardware within seconds, making it ideal for time-critical, on-site emergency response.