## 4 Results  
The performance metrics for the four slum classification networks utilized in this research are detailed in Table 3. The findings for the various models were tested using Equations (1)-(3), the results of which indicated that a model utilizing solely remote sensing imagery (RSI), specifically the Fully Convolutional Network (FCN), attained an intersection over union (IoU) of 57.28. In contrast, the FCN with Deep Kernel (FCN DK) recorded an IoU of 54.47, while the network that combines both RSI and street view imagery (SVI), referred to as FCN-DK, attained an IoU of 86.01, and the standard FCN reached an IoU of 78.89. Notably, the FCN-DK model, which integrates both types of imagery, demonstrated superior accuracy relative to the FCN alone, achieving the highest F1 score and IoU. This enhancement in performance can be attributed to a 1.83% increase in recall when comparing the FCN-DK to the FCN. Concurrently, the field survey results were subjected to testing using decision tree modeling. The results of the model evaluation on the 2024 survey data demonstrated outstanding classification performance, attaining perfect accuracy. As demonstrated by the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Input Data** & **Precision** & **Recall** & **F1 Score** & **IoU** \\ \hline FCN & RSI & 0.8429 & 0.6413 & 0.7284 & 0.5728 \\ \hline FCN-DK & RSI & 0.6837 & 0.7283 & 0.7053 & 0.5447 \\ \hline FCN & RSI \& SVI & 0.7907 & 0.9793 & 0.8723 & 0.8528 \\ \hline FCN-DK & RSI \& SVI & 0.8625 & 0.9976 & 0.9191 & 0.8601 \\ \hline FIELD SURVEY & SURVEY & 1 & 1 & 1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy metrics (precision, recall, F1 score, IoU) for four experimental setups: FCN (RSI only), FCN-DK (RSI only), FCN (RSI + SVI), FCN-DK (RSI + SVI), and field survey results. confusion matrix, the 435 samples classified as 'light slum' and the 27 samples designated as'medium slum' were accurately identified, with no errors observed in the prediction process. This finding underscores the survey's high reliability, even when evaluated on unseen data. A comparison of the confusion matrix of FCN-DK (RSI + SVI) model results with field survey results in Figure 5 reveals that the former processed a substantially larger dataset of 2323 samples in comparison to the 462 samples utilized in the field survey. In the context of slum classification, the FCN-DK model demonstrated a 99.5% accuracy rate by accurately categorising 2283 light slum areas, with a mere 11 misclassifications as medium slum areas. In contrast, the field survey exhibited a perfect classification of all 435 light slum areas, attaining 100% accuracy. For medium slum classification, the FCN-DK model correctly identified 29 medium slum areas with no misclassification, while the field survey identified 27 medium slum areas, also with no misclassification. Both methodologies demonstrate impressive efficacy in accurately identifying medium slum areas, achieving a remarkable zero false negatives, meaning that there were no instances where medium slum areas were incorrectly classified as light slum in either approach. This indicates a high level of precision in the classification process. Notably, the FCN-DK model exhibits a slight inclination toward misclassification, which can be interpreted as a conservative approach. This tendency results in a higher likelihood of categorizing certain areas as higher-density slums rather than overlooking genuine slum regions. Such a strategy may prioritize the identification of at-risk populations, ensuring that no significant slum areas are overlooked, even if it leads to some over-classification. This effective classification performance is further supported by the training and validation loss trends observed in the Fully Convolutional Network (FCN) and Fully Convolutional Network with Dilated Kernels (FCN-DK) model, as illustrated in Figures 6 and 7.