## 2 Materials and Methods
### Step III: Classifying Satellite Imagery for Generating LULC Maps  
The third step of this study aimed to classify satellite images, detect changes in the built-up area over the years, and utilize the classification results in urban growth simulation. The methodology for satellite image classification encompassed four main steps: selection of satellite images, classification of satellite images, accuracy assessment of classified images, and analysis of LULC area changes. The selection, classification, and accuracy assessment of satellite images were conducted using the GEE platform. The flowchart summarizing these four steps is depicted in Figure 7.  
In this study, Landsat 7 images were used for temporal analysis of LULC area changes. Cloud-free and snow cover-free composite images at five-year intervals (2022, 2017, 2012, and 2007) for the spring and summer months were compiled from atmospherically corrected reflectance (SR) Landsat 7 images with a 30 m spatial resolution on the GEE catalog (Path: 175-174, Row: 35-34). A vector file (.shp) delineating the study area boundaries was uploaded to the GEE platform. The classification analysis utilized the normalized difference vegetation index (NDVI), the normalized difference built-up index (NDBI), and combinations of natural color (RGB) spectral indices, with preliminary findings indicating the highest classification success from the RGB composite images. The classification study was conducted considering five primary LULC classes. These classes, labeled on the images, included: (1) Built-up areas, encompassing artificial surfaces such as human settlements, developed areas, and concrete-covered areas; (2) agricultural areas, comprising annual and permanent crops, grasslands, and greenhouses; (3) forest areas; (4) water surfaces, including rivers and lakes; and (5) non-agricultural non-forest areas, consisting of areas with sparse vegetation, sandy or rocky terrain. The classification process aimed to assign each pixel to the appropriate LULC class automatically. However, for supervised image classification, samples of relevant LULC classes need to be provided as training data to the classifier algorithm. Marking selected LULC classes on the images is a prerequisite for training supervised classification algorithms [19]. Therefore, a total of 840 pixels were labeled on images for each year, with 140 pixels randomly distributed for training and 70 pixels for testing per class. These pixels were equally distributed among the five LULC classes. The selected training pixels were used to train the classifier, while the remaining test pixels were used to evaluate the classifier's performance [37; 38]. These pixels were chosen through a visual method enhanced by high-resolution images from Bing and Google Earth, L7 NDVI profiles, false color composites, and local LULC maps. Figure 8 displays the training and test pixels associated with each image composite. The GEE platform offers built-in functions that allow for the use of classification algorithms such as support vector machines, RF, Naive Bayes, and decision trees. In this study, the RF classifier algorithm was utilized, which has been widely used in the literature and has been reported to provide higher classification performance [39; 40]. The _ee.Classifier.SmileRandomForest_ built-in function in GEE was used to generate the classifier model, and hyperparameter tuning was performed. The GridSearchCV method was employed to determine the combination of hyperparameter values of the RF classifier that  
Figure 7: The flowchart summarizing the satellite image classification step. provide the best performance. This hyperparameter optimization was conducted separately for the classifier model of each image for different years. The confusion matrix method has been commonly used to evaluate the performance of classifiers by comparing the outputs of classified images with test data. Confusion matrices contain values representing the classified and true class labels.