# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 2 Methodology
### Machine Learning Algorithms and Classification  
Two machine learning algorithms were assessed: Random Forest (RF) and Classification and Regression Tree (CART). These algorithms were chosen because they all rely on decision trees and can be compared. RF is an ensemble learning technique based on decision trees [26], suitable for categorical data, unbalanced data, and data with missing values [27], and it is renowned for its high accuracy and robustness in handling remote sensing data [28]. The CART classifier algorithm is a commonly used tree-based frame  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Categories**} & \multicolumn{1}{c}{**Description**} \\ \hline Agriculture & Paddy field, farm or farmland, orchard, plant nursery \\ \hline Forest & Forest, nature reserve, big tree \\ \hline \multirow{2}{*}{Grassland} & Grass, meadow, golf, sports center, \\  & grassland, football fields, baseball fields, airport fields \\ \hline \multirow{2}{*}{Mixed} & Recreation ground, residential green, riparian zone, \\  & disc golf course, garden, park, campsite, cemeteries \\ \hline Shrub & Small trees, persistent woody stems above the ground \\ \hline Wetland & Wetland, mangroves \\ \hline \hline \end{tabular}
\end{table}
Table 1: Urban green space categories.  
Figure 2: A street view image of a specific area in the part of the study area (North Jakarta) was utilized as a point of reference to ensure precision and verify its current condition. Source: Google Street View ([[https://shorturl.at/nsFWX](https://shorturl.at/nsFWX)]([https://shorturl.at/nsFWX](https://shorturl.at/nsFWX)), (accessed on 12 July 2023)).  
work in remote sensing applications [29]. It starts by examining all input variables and identifying the optimal binary split of a single variable that minimizes the deviation in the output variable. In the case of the classification of Earth observation data, the input variables include spectral and additional data, whether they are continuous or categorical, and the output variable is the list of land use and land cover classes [30]. The crucial step in determining the structure of a decision tree is choosing an input feature and a threshold value for each split [31].  
Before initiating the classification process, it is imperative to consolidate all datasets. To elaborate, this consolidation encompassed two polarization channels from Sentinel-1; nine spectral bands from Sentinel-2; eight bands from PlanetScope; and two specific indices, namely RTVI and RTWI. Subsequently, the training dataset was employed to train the classifier algorithms, culminating in the execution of the classification process.  
The unit analysis for classification involved the use of administrative boundaries. These administrative boundaries were derived from the Global Administrative Unit Layers (GAUL) Level-2 dataset [32], which is accessible through the GEE data catalog. There are a total of five administrative boundaries, namely North Jakarta, West Jakarta, Central Jakarta, East Jakarta, and South Jakarta. The classification results were subsequently clipped based on these administrative boundaries.