# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 1 Introduction  
Flood, and especially flash flood, is still one of the most harmful natural disasters, resulting in death, population displacement, and economic damage [1, 2]. However, the impact is growing due to climate change, urbanization, and poor risk management. It is important to identify the location and spatial reach of floods in a timely and precise manner for effective disaster management, emergency response, and flood mitigation purposes [3]. However, remote sensing is a crucial technique in the area of geoscience and particularly flood monitoring, for it offers spatial information which is both large-scaleand up-to-date [4; 5; 6]. In addition, SAR sensor systems (such as the ones on board the Sentinel-1A) have unique capabilities for flood detection [7]. However, SAR data provides advantages such as side-looking capabilities and the ability to collect surface information irrespective of weather and lighting conditions [8; 9]. Nonetheless, flood detection in urban settings presents difficulties due to significant double scattering between structures and neighboring ground surfaces [10; 11]. In metropolitan regions, structures generate radar shadows and layover, obscuring substantial parts of the ground surface from Synthetic Aperture Radar [12; 13]. As a result, numerous studies have focused on analyzing the discrepancies in backscatter between SAR pictures acquired prior to and after floods [7; 14; 15; 16]. To conclude, this research is based on the premise that double scattering caused by structures and adjacent floodwater in post-flood photos generally exceeds that from buildings and surrounding non-flooded terrain in pre-flood images [17]. In recent years, deep-learning models have gained significant attention in remote sensing due to their capability to extract features and learn both local and global hierarchies autonomously. For flood detection with bi-temporal SAR images, deep-learning techniques offer advantages like the ability to learn interactions between pre- and post-flood images [18]. In contrast to traditional machine-learning classifiers, deep-learning methods provide enhanced feature representation that effectively addresses issues of speckle noise and complex land cover, particularly in SAR images. Furthermore, deep-learning models can be tailored with modular computational components that specifically address the intricacies of the problem. Importantly, these models are often trained end to end, eliminating the need to address sub-problems (such as building shadows, layover, backscatter similarity in wetland, permanent water, and flooded regions) separately. Remote-sensing-based flood mapping has been enriched by deep-learning techniques, including Convolutional Neural Networks (CNNs), U-Net architectures, Vision Transformers (ViTs), and hybrids that integrate these models. The development from old-style CNN networks to modern architectures is a kind of procedure that consistently works for making local feature extraction and global context understanding in agreement. Convolutional Neural Networks CNNs have long been used as a building block in flood detection models because of their ability to extract local spatial features effectively using convolutional kernels that detect pixel neighborhoods and patterns [19; 20]. Initial attempts, like the patch-based CNN proposed by [21], also demonstrated flood detection on SAR imagery quite efficiently. Expanding on this concept, [PERSON] [22] leveraged 3D CNNs to fuse the temporal information in the multitemporal SAR images and recovered the evolution of floods over time. Notwithstanding the above achievements, a typical CNN architecture usually involves multi-stage pipelines, as demonstrated in [19]. [PERSON] [15] first extracted water bodies and then conducted the change detection in temporal images. While this modular approach facilitates explainability, it is prone to segmentation artifacts [23; 24], leading to suboptimal accuracy.