# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 2 Study Area and Flood Detection Datasets
### Flood Detection Datasets  
#### 2.2.1 Erbil Flood Dataset  
The satellite images from the Sentinel-1 mission were employed in the current research for flood delineation. Based on the Copernicus program of the European Space Agency, the Sentinel-1 offers high-resolution SAR images and includes the possibility of using it for flood monitoring. Differing from the optical images, SAR is insensitive to cloud or illumination, and is suitable for the long-period monitoring of the flood [63]. The study location is Erbil City due to a flash flood that hit the city in December 2021, which resulted in remarkable losses in lives and properties. In the present study, the flood-impinged land surface alteration maps were obtained from the pre-and post-flood SAR scenes (Figure 2). These images were sourced through Google Earth Engine (GEE), a cloud computing platform that specializes in processing geospatial data sets [64], privacy-enabled remote-sensing imagery processing, and analysis at scale using Google Earth Engine.  
Table 1 presents a summary of the pre- and post-flood preparation process for Sentinel-1 images for assessing the impact of the flash flood. The acquisitions of data were collected in the Interferometric Wide Swath (IW) beam mode, which provides a wide swath at medium resolution and is considered appropriate for large-scale disaster monitoring. The high-resolution (HR) data (10 m \(\times\) 10 m spatial resolution) can ensure the precision monitoring of waters, land sinks, and infrastructure damages. This method, with a swath-width of 250 km per swath, can scale large regions with a flooding disaster.  
Figure 1: Map of the study area.  
The raw radar data from Sentinel-1 goes through a preprocessing phase, which involves standard procedures to prepare for further analysis. The first step is processing this raw data, including radiometric calibration to adjust for sensor and atmospheric influences, ensuring that the signal strength accurately reflects surface characteristics. Afterward, geometric correction aligns the data with the Earth's surface, compensating for distortions caused by the satellite's movement and the curvature of the Earth. Speckle filtering is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Acquisition Date** & **Processing Level** & **Polarization** & **Spatial Resolution (m)** \\ \hline \multirow{2}{*}{Sentinel-1} & Pre-flood & \multirow{2}{*}{Level 1} & \multirow{2}{*}{Single—VH} & \multirow{2}{*}{10} \\  & 25 September 2021 & & & \\ \hline \multirow{2}{*}{Sentinel-1} & Post-flood & \multirow{2}{*}{Level 1} & \multirow{2}{*}{Single—VH} & \multirow{2}{*}{10} \\  & 20 December 2021 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Satellite data from Sentinel-1 used for flood analysis.  
Figure 2: Maps of the pre-flood, post-flood, and flood ground truth datasets.  
applied to reduce noise from the radar signal interacting with the surface, enhancing the visibility of features such as bodies of water or buildings. Speckle noise commonly affects SAR images, complicating visual analysis. Lee (5 \(\times\) 5) speckle filtering is implemented to mitigate this noise while preserving image details. Additionally, orthorectification corrects distortions caused by terrain variations, ensuring that the resulting images accurately represent the Earth's surface. The data may also be subsetted based on specific use cases, such as masking flooded areas for targeted data analysis by researchers. This preprocessing is crucial for improving the quality and applicability of satellite data in monitoring events like flash floods. All preprocessing steps of the Sentinel-1 dataset were implemented using JavaScript in GEE.