Improving Cross‐Event Generalization for SAR‐Based Coastal Inundation Mapping via Test‐Time Domain Adaptation

Abstract

Rapid and reliable coastal inundation mapping from Synthetic Aperture Radar (SAR) imagery is vital for disaster response. Although recent deep learning advances have enhanced this task's accuracy and automation, two major challenges remain: (a) geographic and imaging variations across flood events limit the generalizability of supervised models trained on static data sets, and (b) constructing large‐scale annotated flood data sets remains costly and time‐consuming. These challenges are especially pronounced in coastal regions, where precise segmentation is critical but conventional models often struggle to adapt to unseen scenarios, leading to degraded performance. To address this, we propose Coastal Inundation Mapping via Test‐Time Domain Adaptation (CIM‐TTDA) for SAR‐based coastal flood mapping. Instead of requiring new labels, CIM‐TTDA adapts a pretrained water segmentation model to each unseen event using only unlabeled post‐event SAR imagery. The core of CIM‐TTDA is a self‐supervised learning strategy tailored for coastal inundation mapping. Built upon pseudo‐label learning and exponential moving average updates, the strategy ensures stable adaptation across diverse flood events while maintaining computational efficiency. We evaluate CIM‐TTDA on three cyclone‐induced flood events (eight distinct scenes) and conduct five comprehensive experiments to assess its effectiveness, robustness, and efficiency. Results show that CIM‐TTDA improves pixel‐level mean Intersection over Union (IoU) by approximately 0.15 over conventional supervised models across various convolutional‐ and Transformer‐based architectures. With the EfficientNet‐B0 architecture as the pretrained model, CIM‐TTDA achieves a mean IoU of 0.803, outperforming supervised baselines and state‐of‐the‐art test‐time domain adaptation methods. Overall, CIM‐TTDA offers a promising solution for accurate and stable coastal inundation mapping in unseen real‐world scenarios.