# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 1 Introduction
It can be concluded from these attention-driven changes that (i) feature weighting should be emphasized when noise is severe, and (ii) reducing noise is equally important for successful flood mapping. In addition, U2-Net [38] introduces a two-level nested U-structure with Residual U-blocks (RSUs) to gather multiscale context information with affordable computation and proficiency, facilitating the training from scratch without the requirement of pretrained backbones. Ref. [39] further demonstrated the effectiveness of U-Net with ResNet50 backbones on Sentinel-1 flood data, with a uniform high \(F1\)-scores (approximately 0.82-0.83), which emphasized the robustness of the U-Net to different types of floods. Taken together, all these studies show a clear trend: although U-Net remains the backbone for flood segmentation, the introduction of attention and multiscale fusion modules are necessary to adequately handle SAR image noise and complex flood morphologies. Transformers have drawn much attention towards flood detection, since they can model long-range dependencies and the global context using self-attention [40]. Vision Transformers (ViTs) [41; 42] and their hierarchical counterparts, e.g., Swin Transformer [43],address the fixed receptive fields left by the CNNs, therefore supporting a more comprehensive exploration into large flood events. A study by [44] demonstrated that ViT models combined with transfer learning on Sentinel-1 SAR and Sentinel-2 multispectral imagery can outperform traditional CNN model accuracy by up to 15%. This demonstrates the potential role of transformers in recognizing global patterns that are important to understand for mitigating flooding risks and for enabling emergency responses. However, ViTs typically need large-scale training data and struggle to model subtle local patterns due to the absent of convolutional inductive priors [40; 45]. To overcome these limitations, [PERSON] [40] proposed a hybrid transformer model, which is a mixture of a mixer transformer encoder with noise filtering and multiscale depth-wise convolution blocks. This architecture properly decouples the global context modeling and local spatial detail preserving, which tackles problems such as flood-background similarity and edge discontinuity, leading to the best performance on overall benchmark datasets. Other methods include CSWin-U-Net [46], which uses cross-shaped window self-attention and CASCADE upsampling to improve segmentation quality and computational complexity. AgileFormer [47] improves the flexibility by incorporating deformable patch embeddings and spatially varied self-attention so that the irregular-shaped flood regions can be accurately segmented, which is one of the common problems in different shapes of flood landscapes. Furthermore, [PERSON] [48] successfully leveraged Swin Transformer layers in a U-Net pipeline for image restoration, showing that transformers have the potential to advance high-resolution vision tasks, such as fine-grained flood mapping. Recognizing that neither CNNs nor transformers alone sufficiently tackle flood detection issues, hybrid architectures merging their complementary advantages have become the state of the art. These models effectively combine CNNs' local spatial feature extraction with transformers' global context modeling to capture intricate details and extensive dependencies. Recently, TransUNet [49] takes this integration to a new level by substituting the traditional U-Net encoder with a ViT module, which leverages CNNs to encode low-level local details and transformers to capture long-distance context information. [PERSON] [50] extended this idea by creating SwinUNet, a transformer-only version of U-Net, introducing shifted window attention to improve computational efficiency while maintaining the segmentation performance.