## 5 Discussion
### Comparative Analysis of Slum Classification  
The presence of street view and features from remote sensing provide important information regarding land surface, water table, and building density, all of which contribute to a better understanding of slums in urban areas and improve the prediction accuracy of FCN-DK. The yellow circle in Figure 10 indicates that FCN outperforms in locations with SVI. As illustrated in the Figure 10, the locations indicated by yellow circles denote areas in which FCN-DK exhibits superior performance compared to FCN due to the availability of Google street view locations.  
Meanwhile, as demonstrated in Figure 11, the areas highlighted in blue illustrate the regions where the FCN and FCN-DK models exhibit substandard performance, attributed to their restricted access to GSV locations. Conversely, the areas highlighted in yellow show the regions where FCN-DK demonstrates superior performance compared to FCN, due to its enhanced access to GSV locations.  
Figure 11: Discrepancies in slum classification due to limited SVI access, with blue indicating under-performing areas and yellow showing improved accuracy with SVI integration: (**a**) Slum Detected by FCN-DK with RSI & SVI, (**b**) Slum Detected by FCN with RSI & SVI, and (**c**) Slum Detected by FCN-DK with RSI Only  
Figure 10: Spatial comparison of slum detection results between FCN and FCN-DK models, highlighting areas with street view imagery (SVI) coverage.  
Figure 11 reveals that the integration of SVI has the potential to improve the identification of various criteria that signify the presence of slum areas. In Figure 11 column c, which is the result of slum mapping using RSI alone, there is a light slum classification in one area, while in columns a and b, it can be seen that the addition of SVI provides more detailed information that causes the model to classify several points as moderate slums in the same area. This indicates that the integration of SVI has the ability to capture the characteristics of slums that may not be detected by RSI, such as the conformity of buildings to prescribed technical standards and the condition of neighbourhood drainage, as well as the quality of nearby roads. In this research, the approach used is a Convolutional Neural Network (CNN), which was chosen for its architectural simplicity in accordance with the characteristics of the available dataset, which consists of only two categories, namely light slums and medium slums. It was confirmed from an interview with the local government that these categories have been determined in Bandung City since 2020 due to various efforts to deal with slums through the KOTAKU (Kota Tampa Kumuh) Programme from 2018 to 2022.  
In comparison to the results of field survey mapping (Figure 12), the classification of slum areas provided by field survey mapping is conservative and subjective, with areas described as'mild slums' not distinguished by severity. Conversely, the FCN-DK model provides a more detailed and automated classification, frequently identifying'moderate slum' conditions in areas that are considered to have light slum characteristics by field surveys. This discrepancy may be attributed to the model's utilization of high-resolution imagery and street-level views, which facilitate the detection of features such as building density and road conditions that may be overlooked by field surveys due to constraints such as accessibility and subjectivity.  
Figure 12: Spatial distribution of slum areas on field survey results.