## 3 Results
### Comparison of Models
#### 3.1.3 Qualitative Evaluation  
Visualizations of the flood inundation maps produced by each model for each image type are provided in Figures 5-8, with Figures 5 and 7 depicting an example image taken in clear conditions by Sentinel-1 and Sentinel-2, respectively, and Figures 6 and 8 depicting images taken in cloud-covered conditions, providing further insight into how well each model was able to handle different weather conditions present in the imagery. These visualizations revealed that the models exhibiting superior performance against the evaluation metrics tended to produce more refined segmentation masks, resulting in more detailed maps. Conversely, models with weaker performance tended to generate coarser masks, characterized by a higher incidence of false-positive pixel classifications. Notably, the inundation maps produced by the proposed model consistently aligned more closely with the ground  
Figure 6: Sentinel-2 input image, ground truth, and predicted flood inundation maps for an example image in clear conditions. Figure 7: Sentinel-1 input image, ground truth, and predicted flood inundation maps for an example image in cloud-covered conditions. In Figure 8, depicting an image sourced from Sentinel-2 under cloudy conditions, the sensor's visibility was limited, introducing noise into the image. However, even under such challenging circumstances, the proposed model managed to create the most accurate inundation map. This model, in particular, exhibited fewer false-negative areas, which is a crucial factor as it reduces the risk of missing instances of flooding in real-world applications. To shed light on the decision-making process of the models, the heatmaps generated through gradient-weighted class activation mapping highlighted the areas of the image where the model placed the most importance when making predictions. The color-coded heatmap provided valuable insights into the extent of the model's attention, where red areas received the highest focus, and dark blue areas received the least. This feature offered more informative data compared to the binary ground truth, as it quantified the presence and significance of the identified features. The heatmaps for models trained with Sentinel-1 are displayed in Figures 9 and 10, while Figures 11 and 12 illustrate the heatmaps for models trained with Sentinel-2. In general, heatmaps from high-performing models exhibited closer alignment with the ground truth and assigned a higher magnitude of importance to relevant pixels. An exception to this pattern was observed in the case of U-Net++ with the ResNet50 backbone trained with Sentinel-1 images, where the heatmaps revealed the model's attention to a significant number of irrelevant pixels. Nevertheless, the magnitude of importance attributed to these pixels is minimal, and the areas of higher importance closely corresponded to the ground truth. These heatmaps further emphasize the superior quality of the proposed model, as not only is it able to provide the most accurate flood inundation map, but it is also demonstrably making the most appropriate decisions, applying the highest magnitude of importance to the most relevant pixels of each of the compared models. Figure 8: Sentinel-2 input image, ground truth, and predicted flood inundation maps for an example image in cloud-covered conditions. Figure 10: Sentinel-2 input image, ground truth, and heatmap explanations, with higher temperature (with blue being a low temperature and red a high temperature) indicating greater feature importance, for an example image in clear conditions. Figure 9: Sentinel-1 input image, ground truth, and heatmap explanations, with higher temperature (with blue being a low temperature and red a high temperature) indicating greater feature importance, for an example image in clear conditions.