## 2 Materials and Methods
### Identification of Urban LULC and Change Detection
The same classification methodology was used for each year, but the auxiliary data and producer for the classification process  
Figure 6: Representation of change detection in Urban Atlas class according to the consecutive years. were different. So, this might explain the difference in changes between these classes. A similar situation was also observed for the forests class (code 30000) due to the classification process, since the zoo area was classified as a forest area for 2010 and 2015, and was revised to a green, sports, and leisure facilities class (code 14000). However, the forest area in the northwest of the study area was identified as forest with the forest type maps in the classification process for the years 2010 and 2015, and the revision process was not carried out for the Urban Atlas. Therefore, classifications into the forests class (code 30000) in 2010 and 2015 was more than in the other years. When spatio-temporal changes were examined with visual interpretation, an increase was observed in the construction areas class (code 13100) in 2018. Also, the construction areas of urban fabric and roads and associated land classes were completed in 2010. As of 2018, the construction of urban fabric and commercial areas started in the west and north of the city center. In addition, the construction areas on the north side were completed and assigned to the commercial area class (code 13100). In order to compare the data with similar data production stages, the Urban Atlas 2010 and 2015 and the Urban Atlas 2012 and 2018 were compared. In Figure 7, the changes in the class data produced with the same processing steps can be seen. In this analysis, there was a change in the direction of increase in the areas of the classes, and it was similar to the change in consecutive years. However, the magnitude of the changes in the construction areas class (code 13100) and arable land class (code 20000) was also demonstrated. Also, there was an increase in class areas with spatio-temporal data. In general, there was an increase in area value for classes with multi-temporal data, but the value of the arable land class (code 20000) area was decreased due to the use of arable land for different purposes. For the construction area class (code 13100), the amount of change between 2012 and 2018 was approximately 12 times the amount of change between 2010 and 2015, which was the highest change for Gaziantep. Similarly, the amount of change between 2012 and 2018 was higher than the amount of change between 2010 and 2015 for the arable land class, the urban fabric class, and the industrial, commercial, public, military, and private class (code 12000), at 4, 1.7, and 2 times, respectively. However, the change in the mine extraction and dump site class (code 13000) was from 12.6 ha between 2010 and 2015 to 115.9 ha between 2012 and 2018. Due to the classification process, the areas of the forest class (code 30000) in the Urban Atlas 2010 and 2015 were higher than in the Urban Atlas 2012 and 2018. However, there was an increase as well as yearly differences for the forest class (code 30000) area. Therefore, it was interpreted that urban forest areas had increased from past to present. In Figure 7, the area changes for each class in 2012 and 2018 were more than the changes in 2010 and 2015. Therefore, long-term changes in the Urban Atlas show that there was more change in the city center between 2012 and 2018. The closest changes were  
Figure 7: Representation of change detection in nine Urban Atlas cases according to same methodology. in the road and association land (codes 12100), mineral extraction and dump sites (code 13000), green urban areas, sports, and leisure facilities (code 14000), and water (code 50000) classes. As a result of the analyses, the increase in classes such as urban fabric, construction sites, industrial, commercial, public, military, and private also indicates that the city center continued to develop and sprawl with the population.