## 2 Materials and Methods
### Methodology and Sources of Land Use/Land Cover Data
Forest type map, Open Street Map, CORINE 2012, and online map (e.g., Google Earth, Yandex) data were used for the sample dataset and visual interpretation stages. Furthermore, in the classification process, various functions provided by the eCognition Developer 9 software and NDVI and NDWI indices were utilized to classify surface objects. The asymmetry, rectangular fit, and relations of the border to functions were used, which enable the classification of objects based on the geometric shapes of segments [43]. The overall accuracy of classification was 95% for the 2010 Spot 5 image and found to be 96.27% with a kappa value of 0.957 for the 2015 Spot 6 image. The classification accuracy of the classes was between 90% and 98% for urban classes and between 90% and 99% for rural classes. The comparison of the Urban Atlas for 2010 and 2015 and Urban Atlas for 2012 and 2018 shows that the geometric shapes of the objects are similar in the city center; however, the crossroads were more detailed in the Urban Atlas for 2010 and 2015 due to traces and paths. In the years of production for the Urban Atlas 2010 and 2015, the Urban Atlas methodology had 20 classes for LULC. The new subclasses were not included in Urban Atlas for 2010 and 2015, so the features of the subclasses were combined in order to determine the changes in the main classes for each year. Therefore, change detections were achieved with nine classes for four years. The aim of this study was to emphasize the relationship between the change in artificial objects and environmental effects in settlement. The classes were generalized into nine main classes. The main urban fabric class (code 11000) was created by combining the 3 rd-level classes (codes 11100-11240) of the urban fabric class and the isolated structures class (codes 11300). The main road class (code 12100) was created by combining the 3 rd-level classes of the road classes (codes 12210-12230), port class (code 12300), and airport class (code 12400). The construction sites class (code 13300) and land without current use class (code 13400) were combined to create the main construction site class (codes 13100). Also, the green urban areas and sports class (code 14100) and leisure facilities class (code 14200) were combined to create the main green area class (codes 14000). Finally, the arable, permanent crops, pastures, complex and mixed cultivation patterns, and orchards classes (codes 21000-25000); the herbaceous vegetation associations and open spaces with little or no vegetation classes (codes 32000-33000); and the wetlands class (codes 40000) were combined to define the semi-natural and wetland areas class (code 2000). Figure 2 shows the original Urban Atlas nomenclature and the Urban Atlas nomenclature for this study, which include 27 and 9 classes, respectively. Inspection of the Urban Atlas for 2012 and 2018 showed that the commercial, military, and green areas (zoo) were protected within the same borders for both years, where these borders were specified as the Civil Administrative Boundary (Figure 3). Therefore, the boundaries of commercial and green areas were revised for the Urban Atlas 2010 and 2015 according to the boundaries of the Urban Atlas 2012 and 2018. The change detection analyses were examined with these revised Urban Atlas maps. The revised Urban Atlas map accuracy was also between 80% and 85% for each class. Figure 3 shows the reference (2012 and 2018) and revised (2010 and 2015) boundaries of commercial, military, and green (zoo) areas. Changes among LULC classes can reveal shifts in the population living in the city and their emerging needs and consumption. Needs increase in settlements with the population, and industrial areas could develop for business and production purposes in line with these needs. Moreover, by identifying consumption and need parameters, sustainable information for urban planning is brought to light.