# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 1 Introduction
Reference [51] leverages a CNN-Transformer hybrid encoder for U-Net [31] architecture in combination with a non-linear double upsampling decoder to enhance the feature extraction and generalization for complex scenes, a task particularly relevant to flood segmentation. Reference [52] introduced the CvT-U-Net, which combines convolutional projections with multihead self-attention blocks, effectively balancing spatial localization and global context for an accurate weld pool segmentation, as of potential relevance also for flood boundary detection. In contrast to transformer hybrids, which have strong demand for computation and pretraining resources [53; 54] tackled this problem with the development of FET-U-Net, which includes the CNN (ResNet34) and Swin Transformer branches by high-level feature fusion and multiscale upsampling and achieved more excellent results on the task of ultrasound segmentation--a domain with imaging difficulties similar to flood detection. Lightweight hybrid design for U-Net models, such as those in UNetFormer [55], with a global-local attention mechanism, made it feasible to provide robust accuracy in the real-time semantic segmentation, which is required by flood monitoring. Furthermore, [PERSON] [56] showed that a simple concatenation of transformer and U-Net (Transformer-U-Net) outperforms the depth-wise U-Net models, with some trade-off in efficiency regarding the depth of the backbone. Moreover, [PERSON] [26] proposed a Siamese network using Swin Transformer (SwinTrans) based on hierarchical feature extractors, the main power is computational efficiency, which is accompanied by spatial connectivity to SAR flood image detection. Reference [57] proposed the ViT-U-Net for high-resolution coastal wetland classification, replacing convolutions with Vision Transformer blocks and introducing dual skip connections and the bilinear polymerization pooling to improve feature fusion, increasing the precision of the original U-Net by more than 4%. Together, these hybrid methodologies represent an evolving research trajectory that effectively combines the strengths of CNNs and transformers, tackling the challenges of flood detection--noise, scale variance, and complex boundaries--with enhanced accuracy and practical viability. This research focuses on developing and rigorously evaluating a novel hybrid deep-learning model that merges the strengths of the U-Net convolutional encoder-decoder structure with a Compact Convolutional Transformer (CCT) tokenizer. This combination allows for accurate and reliable flood detection using Sentinel-1 SAR imagery. The model is designed to effectively capture intricate spatial details and long-range global context, thus addressing the limitations of standalone CNN and Transformer frameworks. To achieve this, the model is trained and tested using benchmark datasets, including Sen1 Floods11 and an additional dataset from the 2021 Erbil flood, facilitating a comprehensive performance assessment across various flood scenarios. The study aims to create a dual-path architecture that employs convolutional tokenization for efficient local-global feature integration, thereby boosting the model's classification accuracy and resilience to noise and variability often found in SAR data. It also benchmarks its performance against existing CNN, Transformer, and hybrid models, conducts ablation studies to evaluate the contribution of each component, and illustrates its potential for real-time flood monitoring and early warning systems. This study's main contributions are summarized below:  
1. The model combines a U-Net convolutional path for precise spatial and contextual feature extraction with a Transformer branch employing a CCT tokenizer to grasp sequential long-range dependencies and global context. 2. The model minimizes parameter needs and enhances generalization on small datasets commonly associated with flood detection by employing the CCT tokenizer instead of traditional Vision Transformer tokenization. 3.