## 1 Introduction
### Previous Work
#### 1.1.2 Artificial Intelligence for Flood Inundation Mapping  
Recent advancements in machine learning and computer vision, coupled with increased computing power and extensive satellite data from programs like Copernicus, have spurred research into artificial intelligence's application in flood inundation mapping. Although SAR images are commonly used due to their all-weather capability, some studies highlight the better performance of optical imagery. For instance, [PERSON] et al. [16] observed higher mean IOU values for Sentinel-2 compared to Sentinel-1. Similarly, [PERSON] et al. [17] reported higher F1 scores for Sentinel-2.  
Traditional machine learning models have yielded accuracies ranging from 70% to 90%, but deep learning has demonstrated superior performance [18; 19; 20; 21; 22]. Deep learning models, particularly Convolutional Neural Networks (CNN), have been widely adopted for flood inundation mapping due to their ability to learn complex, non-linear relationships directly from raw data. They eliminate the need for extensive preprocessing, such as feature engineering. Models based on fully convolutional networks (FCN) have outperformed classical machine learning and traditional techniques [23].  
To address resolution limitations introduced by convolutional layers, encoder-decoder architectures have been proposed. The U-Net model, a popular choice, features in several studies [5; 17; 24; 25; 26; 27]. U-Net++ is a further state-of-the-art model that expands on U-Net, and it has been shown to achieve impressive performance in flood inundation mapping [29; 30]; however, [PERSON] et al. [27] found that the standard U-Net performed better. Several studies have improved the performance of this architecture by incorporating attention mechanisms [31; 32], demonstrably providing superior accuracy and efficiency.  
Other architectures, like DeepLabV3 and DeepLabV3+, introduce innovative features such as atrous convolution, spatial pyramid pooling, and separable convolution, and they have been shown to outperform U-Net [27]. The choice of architecture and encoder module plays a critical role in model performance.  
For SAR imagery, novel architectures have been introduced to improve performance. FWENet [33] combines aspects of U-Net and DeepLab, incorporating atrous convolution for enhanced results. Siam-DWENet [34], a Siamese network, learns differences between SAR images at various time intervals, outperforming other architectures. In optical imagery, H2O-Net [35] leverages generative adversarial networks to map RGB bands to SWIR signals, outperforming existing architectures trained with the original RGB bands.  
One challenge in utilizing deep learning for flood inundation mapping is the need for large, labeled datasets, which can be time-consuming and costly to obtain. Efforts have been made to curate datasets like the Sen1 Floods11 dataset. This paper aims to develop a deep learning semantic-segmentation model suitable for flood inundation mapping. The model's appropriateness for this task as well as its performance are assessed by leveraging the Sen1 Floods11 dataset.  
The contributions of this paper are as follows:  
1. Presenting a novel deep learning semantic-segmentation model capable of producing high quality flood inundation maps from both SAR and optical images, demonstrated through comparative analysis with state-of-the-art models.
2. Investigating various combinations of fusion of SAR and optical spectral bands and indices, providing insight into the optimal combinations for accurate flood inundation maps in both clear and cloud-covered conditions.
3. Integrating XAI to interpret the behavior of the deep learning models, providing a more comprehensive understanding of their capacity to learn effectively. This not only enhances the trustworthiness of the models, but also provides deeper insight into the influence of each input-data type on the models' decision-making process.