FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land-Cover Identification Models

Urban flooding in arid regions threatens infrastructure and public safety. Fine-scale mapping of flood extents is vital for effective emergency response and resilience planning, but limited spectral contrast, rapid hydrological changes, and heterogeneous land covers make this task challenging. In this work, we introduce FM-LC, a hierarchical framework for flood mapping by land-cover identification on high-resolution, daily PlanetScope imagery. Through a three-stage process, it first uses an initial multiclass U-Net to segment imagery into water, vegetation, built area, and bare ground classes. We identify that this method has confusion between spectrally similar categories (e.g., water versus vegetation). Second, by early checking, the class with the major misclassified area is flagged, and a lightweight binary “expert” segmentation model is trained to distinguish the flagged class from the rest. Third, a Bayesian smoothing step refines boundaries and removes spurious noise by leveraging nearby pixel information. We validate the framework on the April 2024 Dubai storm event, demonstrating average $F1$ -score improvements of up to 29% across all land-cover classes and notably sharper flood delineations. Compared to conventional single-stage U-Nets, FM-LC achieves over 12% higher mean $F1$ , significant gains for vegetation classification, and more reliable temporal tracking of flood dynamics. These results highlight FM-LC as a practical and scalable solution for high-resolution flood mapping in complex urban and arid environments.