## 3 Materials and Methods
### Classification Using only RSI  
The FCN-DK model was trained using randomly selected initial weights and a maximum of 100 epochs. Validation accuracy was monitored using the Early Stopping technique with a patience of 10 epochs. This means that if there was no improvement in validation accuracy over the previous 10 epochs, training would stop. However, the model continued to run for the full 100 epochs, as the validation accuracy did not remain constant for 10 consecutive epochs. Detailed information regarding the network configuration parameters for training the FCN-DK model can be seen in Table 2.  
To classify urban scenes from street view imagery in Bandung City, a pre-trained CNN model was adapted. The adaptation involved freezing the first five convolutional layers and fine-tuning only the final classification layer. The Early Stopping technique was used to monitor validation accuracy during ten epochs of training. Training a CNN from scratch requires significant time and access to extensive datasets, making pre-training and adaptation a more resource-efficient approach.  
Classification Using an Integration of SVI with RSI  
In this study, the authors adopt a methodology that merges data obtained from Google street view (GSV) images with remote sensing imagery (RSI) to create a spatially continuous feature map, similar to the work conducted by previous researchers in a different context within Jakarta City [16]. The initial phase involves extracting features from GSV images through a tailored Convolutional Neural Network (CNN). While prior studies have indicated that Principal Component Analysis (PCA) can be advantageous for reducing the dimensionality of the extracted features [16], the authors caution that an overly limited selection of principal components may inadvertently discard valuable information. Thus, it is crucial to carefully determine the appropriate number of principal components, as slum indicators encompass not only road conditions, but also various other factors, including drainage, sanitation, waste management, and fire safety.  
Subsequently, new data points were generated in the cardinal directions--north, south, east, and west--surrounding each GSV location, leading to the creation of distinct feature maps. The final step involved integrating these synthesized feature maps with RSI, culminating in a holistic representation of the urban environment. This approach effectively harnesses the detailed insights provided by GSV imagery while capitalizing on the extensive spatial coverage afforded by RSI, thereby yielding significant contributions to urban environmental analysis.  
In this research, slum classification is conducted using field surveys to identify slums, which involves assessing various indicators such as housing quality, infrastructure, and living conditions. The field survey is based on an existing guideline to evaluate and improve settlements to enhance living standards and prevent further slum development, namely the Minister of Public Works and Housing Regulation Number 14/2018.  
The 2024 slum survey yielded 435 points, categorized as 'light slum', and 27 points, categorized as'medium slum'. The total area encompassed by these points was 248.38 ha, dispersed across 27 sub-districts, 72 urban villages, and 142 areas. Following the survey, the data will undergo a rigorous process of evaluation and validation, with reference to the 2020 slum reference map of Bandung City, to ensure the accuracy and integrity of the results.