FloodKAN: Integrating Kolmogorov–Arnold Networks for Efficient Flood Extent Extraction

Flood events are among the most destructive natural catastrophes worldwide and pose serious threats to socioeconomic systems, ecological environments, and the safety of human life and property. With the advancement of remote sensing technology, synthetic aperture radar (SAR) has provided new means for flood monitoring. However, traditional methods have limitations when dealing with high noise levels and complex terrain backgrounds. To address this issue, in this study, we adopt an improved U-Net model incorporating the Kolmogorov–Arnold Network (KAN), referred to as UKAN, for the efficient extraction of flood inundation extents from multisource remote sensing data. UKAN integrates the efficient nonlinear mapping capabilities of KAN layers with the multiscale feature fusion mechanism of U-Net, enabling better capturing of complex nonlinear relationships and global features. Experiments were conducted on the C2S-MS Floods and MMFlood datasets, and the results indicate that the UKAN model outperforms traditional models in terms of metrics such as the intersection over union (IoU), precision, recall, and F1 score. On the C2S-MS Floods dataset and the MMFlood dataset, UKAN achieves IoUs of 87.95% and 78.31%, respectively, representing improvements of approximately 3.5 and three percentage points, respectively, over those of the traditional U-Net. Moreover, the model has significant advantages in terms of parameter efficiency and computational efficiency. These findings suggest that the UKAN model possesses greater accuracy and robustness in flood inundation area extraction tasks, which is highly important for increasing the monitoring and early warning capabilities of flood disasters.