From flood extent mapping to mechanism-aware flood products: integrating flood type classification into satellite-based flood monitoring

Flood type information is critical for effective flood risk management because dominant flood types are associated with distinct hydrodynamic behaviour, contamination pathways, and recovery trajectories. However, most operational flood mapping products provide only binary inundation extent, offering limited information for interpreting the dominant flood type and its likely impact characteristics. Existing flood type classification approaches rely predominantly on hydrometeorological observations and modelling, which are often unavailable in data-scarce regions and can be unstable in mechanism-complex environments such as estuarine deltas, urban river corridors, and coastal cities. To address these limitations, this study proposes a multi-CNN framework that integrates flood type classification directly into satellite-based flood mapping. The framework first uses a U-Net model for flood extent segmentation and then applies a CNN-based classifier for scene-level flood type identification by combining satellite imagery with auxiliary topographic and hydrological-context features, including DEM information and water connectivity ratios. Several CNN architectures were compared for flood type classification, with Inception-ResNet selected based on its performance–complexity trade-offs. To support model training and evaluation, this study introduces the World Flood Type (WFT) dataset, a new multi-event flood dataset containing 464 flood scenes from 120 flood events across 48 countries. Results show effective performance in both inundation segmentation and flood type classification. The U-Net model achieved 83.8% overall accuracy on an independent test dataset, while the final Inception-ResNet-based classifier achieved 93.0% scene-level overall accuracy and a scene-level macro-F1 score of 0.73 under dominant-mechanism conditions. These findings demonstrate the feasibility of extending conventional flood extent products with scene-level attribution of dominant flood types using remotely sensed imagery and ancillary geospatial data, thereby providing more decision-relevant information for emergency response and post-disaster assessment.