## 4 Discussion  
The proposed model stands out for its capacity to produce flood inundation maps using data from both Sentinel-1 and Sentinel-2 satellites, consistently surpassing the performance of the state-of-the-art segmentation models that were assessed. Notably, the comparative analysis revealed that the U-Net++ with ResNet50 and DeepLabV3+ with MobileNet_V2 models performed better than their counterparts with Sentinel-1 data, whereas U-Net++ with MobileNet_V2 and DeepLabV3+ with ResNet50 performed better with Sentinel-2 data. This presents a challenge when selecting a model for use in a practical setting, as this limits the choice of data. The proposed model, however, consistently demonstrated superior performance across both types of images, making it much more flexible in its practical application. The MA-Net model performed consistently poorly across both data types. It was found in Section 3.1.2 that the computational complexity of this model was significantly higher than the other evaluated models, which given the limited size  
Figure 16: Sentinel-1 image, Sentinel-2 image, ground truth, and heatmap explanations, with higher temperature (with blue being a low temperature and red a high temperature) indicating greater feature importance, from each input-data combination for an example image in cloud-covered conditions. of the dataset can be attributed to its weak performance. The MA-Net model with the MobileNet_V2 backbone achieved superior performance to its ResNet50 counterpart, and its number of parameters was closer to U-Net++ with ResNet50 and the proposed model, which further emphasizes that this model is too complex to perform well with the small dataset that was employed in this study. The visualizations of the flood inundation maps further emphasized the quality of the proposed model, as it was consistently able to distinguish the water from the background pixels more accurately than any other assessed model. This is crucial for demonstrating its real-world suitability, as inaccuracies in the flood inundation maps can have significant impact. In the event of false-positive predictions, resources would be needlessly allocated to areas where there is no flood present. False-negative predictions, on the other hand, have more critical implications, as no measures would be put in place to manage the risk of flooding, which could have perilous consequences on human lives and infrastructure. In examining the impact of the removal of each module of the proposed model, it was found that both deep supervision and weighting exhibited relatively modest effects on performance, suggesting the model's adaptability to the absence of intermediate supervision signals and the shift from weighted to simple averaging in output fusion, respectively. In contrast, dense skip connections and spatial pyramid pooling, which are crucial for contextual information and multi-scale feature capture, showed more pronounced influences on model performance when removed. The absence of dense skip connections disrupted efficient feature transfer, diminishing contextual awareness and detail capture, resulting in a notable reduction in IOU. Similarly, spatial pyramid pooling's removal hindered adaptability to diverse object scales, impacting versatility in recognizing spatial dependencies and leading to a decrease in IOU. Atrous convolution demonstrated a moderate impact, indicating its role in balancing spatial context and resolution. These findings underscore the nuanced interplay of architectural elements and their varied contributions to the model's proficiency in semantic segmentation. Through the quantitative analysis, it was demonstrated that Sentinel-2 images provided a better-performing model; however, the qualitative analysis revealed more nuance which further emphasizes the importance of the versatility of a flood inundation mapping model.