## 3 Materials and Methods
### Methodology
#### 3.2.2 Implementation Stage  
Data Preparation  
The pre-processing of the data involves two distinct phases. Initially, the official slum reference map from 2020 underwent an update based on the slum characteristics outlined in Table 1. This first phase set the groundwork for generating the dataset that would be used for both training and testing in the subsequent phase.  
During the initial phase of data pre-processing, it was also noticed that certain areas in the 2020 official slum reference map were not completely comparable with the slum definitions used in this study as the parameters used in this study matched the features available in the remote sensing imagery. The official map included various developed regions, green spaces, high-rise residential buildings, and industrial zones, which were inaccurately classified as slums. This discrepancy likely arises from the temporal difference between the classification input data, which was collected in 2024, and the slum reference data from 2020. Consequently, the 2020 slum reference map was revised according to the specific slum definition and indicators presented in Table 1. As a result, some areas that were previously categorized as slums in the official map were reclassified, while others that displayed slum characteristics as recognized by local experts were designated as an unknown category of slum. The modified slum map formed the basis for creating high-quality training and testing datasets for further analysis, as illustrated in the accompanying Figure 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Mapping Indicators**} \\ \hline High building density & RSI: NDBI \\ Poor wall materials & SVI: Iron sheets, Wood blocks, Plastic sheets, and Low-quality construction materials \\ \hline Dense area with lesser roads & Ancillary data: Road network data \\  & RSI Shape: Compactness \\ \hline Unplanned layout & RSI Shape: Compactness \\ \hline Unpaved/light roads & Ancillary data: Road network data \\  & RSI Shape: Compactness \\ \hline Small building size/building footprint & Ancillary data: Building footprint data \\  & RSI Shape: Compactness and NDBI, NDBaI, UI \\ \hline Poor roof materials & RSI Tone: Iron sheets and Asbestos sheets \\  & SVI: Iron, Asbestos, and Plastic sheets \\ \hline Proximity to rivers, railroads, swamps, and shrines & Ancillary data: Potential Slum Survey Data 2024 \\  & RSI Association: Proximity to Rivers/ NDMI, NDWI, MNDWI, ManbWI, Railroads, Swamps, and Shrines \\  & SVI: Proximity to Rivers, Railroads, Swamps, and Shrines \\ \hline Near to industrial and warehouse area & RSI Association: Near to the industrial and warehouse area \\  & Ancillary data: Potential Slum Survey Data 2024 \\ \hline Less open and green spaces & RSI Association: Less open and green spaces, NDVI, EVI, SAVI \\  & SVI: Less open and green spaces \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conversion of slum characteristics into mapping indicators for modifying the 2020 official slum reference map.  
The second phase of data pre-processing was dedicated to preparing the data suitable for deep learning frameworks, particularly the Fully Convolutional Network (FCN). In this phase, the study area was methodically segmented to achieve a balanced representation of the two classes, slum and non-slum, derived from the existing dataset, thereby promoting a diverse dataset. This stage aims to partition the extensive study area in Bandung City into smaller segments, facilitating easier processing while ensuring that a balanced representation of classes. This includes both remote sensing imagery and ground truth data, with a designated number of tiles allocated for training and a separate number reserved for testing the proposed FCN network.