## 4 Results
In Figure 6, the downward trajectory of both training and validation loss over time signifies that the model is not only learning effectively but also generalizing well to new, unseen data. The initial high training loss, which progressively declines, reflects the model's capacity to refine its predictions, while the closely aligned validation loss indicates that there are no signs of over-fitting. This strong performance and effective generalization reinforce the model's capability to accurately classify slum areas, aligning with the earlier assertion of its precision and conservative classification tendencies. As the training process concludes, the stabilization of both losses suggests that the model has reached an optimal performance level, ensuring that significant slum areas are reliably identified. Figure 5: Confusion matrix comparing classification accuracy of (**a**) FCN-DK (RSI + SVI) and (**b**) field survey results. Meanwhile, Figure 7 depicts the training loss and validation loss associated with a Fully Convolutional Network with Dilated Kernels (FCN-DK) model utilizing remote sensing imagery over the course of 25 training epochs. At the outset, both loss metrics are elevated; however, they experience a significant decline during the initial epochs, signifying successful learning. Although the losses continue to diminish, the rate of decrease slows over time, ultimately reaching a point of stabilization around the tenth epoch. Notably, throughout the training process, the validation loss consistently remains lower than the training loss, indicating effective generalization and the absence of over-fitting. In the later stages of training, both loss values stabilize, suggesting that the model has converged and shows limited capacity for further enhancement. This observation underscores the model's effective learning capabilities and its strong generalization, potentially influenced by techniques such as regularization or dropout. The elevated accuracy levels achieved through each methodology led to the depiction of the recognized slum areas on the revised slum reference map, which exclusively included the classifications of 'light slum' and'medium slum' as defined by the local government's slum area mapping framework. The findings for both the FCN and FCN-DK approaches are detailed in Table 3, while Figures 5 and 8 provide a visual representation of the outcomes produced by these two methods. Additional analyzes were performed to investigate how different combinations of input datasets influence the comprehension of deep learning models in the classification of various slum categories. The FCN and FCN-DK models demonstrated comparable  
Figure 6: Training and validation loss curves for the Fully Convolutional Network (FCN) model using only remote sensing imagery (RSI). Figure 7: Training and validation loss curves for the FCN-Dilated Kernel (FCN-DK) model using only remote sensing imagery (RSI). performance levels. Subsequently, the prediction outcomes derived from the use of FCN and FCN-DK were categorized into multiple slum classifications, as illustrated in the Figure 9.  
The comparative performance chart (Figure 9) illustrates the effectiveness of various Fully Convolutional Network (FCN) configurations in detecting slum areas against field survey results. The FCN with remote sensing imagery (RSI) identified 1647 light slum and 55 medium slum areas, achieving a recall rate of 64.13%. The FCN-DK with RSI demonstrated enhanced performance, detecting 1545 light slum and 37 medium slum areas with a recall of 72.83%. The incorporation of street view imagery (SVI) into the RSI framework led to a substantial enhancement in the performance of the FCN model, resulting in the identification of 569 light slum and 12 medium slum areas, with a recall rate of 97.93%.