## 3 Study Area and Data  
### Sentinel Dataset  
The study site is an approximately 658 km\({}^{2}\) area located on the European and Asian sides of Istanbul, the Marmaris region in Mugla, and the nearby Golcuk region in Kocaeli, Turkey (Figure 1). As the study area, regions of approximately equal weight including built-up areas, water bodies, and forest and semi natural areas were selected from three different cities. These cities are located in the northwest and south of the country, so they have different urban and forest texture characteristics due to climate, demographic density, and land use differences. These areas were selected to enable the development of a convenient and adaptable methodology that can be applied to as wide a range of areas as possible, for example, in national map products. Training and testing images were selected separately in these regions. One area in Istanbul (Figure 2) and one area in Kocaeli city (Figure 3) were selected for testing. Three different areas in Istanbul (Figure 2) and one area from Mugla (Figure 4) were selected for training, while an area was selected for validation. As satellite imagery, input Sentinel-2A imagery was used in the experiment. Sentinel-2 data can be retrieved from Sentinel-Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), accessed on 21 December 2020) and [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) with free access. Sentinel-2A products are ortho-images in UTM/WGS84 projection. The data acquisition date was 24 May 2020 for all satellite images used in this study. Red, green, blue, and near-infrared (NIR) bands were selected for preparing experiment data in 10 m spatial resolution. The images from Sentinel-2, an Earth observation mission from the Copernicus Programme, were used as input data in our study for the following reasons: (i) the satellite's coverage area is wide (a swath width of 290 km) and used for global acquisitions. (ii) It is a European Space Agency (ESA) mission launched in 2015 and 2017. They provide noncommercial and free data that are multispectral imagery with 13 bands with 443-2190 nm, allowing the production many indices via bands for classifying LULC maps, land monitoring, and various estimations. Sentinel images are quite proper for regional monitoring programs in terms of spatial, spectral, and high temporal resolution (for a single Sentinel-2 satellite, it is 10 days and a combined constellation revisit frequency of 5 days). In deep learning, producing thematic maps with VHR images requires expensive GPU units and computer system requirements. Besides, Sentinel-2 imagery is convenient for thematic map producing requirements and does not require as much heavy processing as VHR images. The study contains three classes of the first level of LCCS. The artificial surfaces class includes urban fabric, industrial units, dumps, construction, mining sites, and artificially  
Figure 4: Subset image of Sentinel-2A for the training area 4 in Mugla city for the experiment in Mugla city. Figure 3: Selected test image of Sentinel-2A in Kocaeli city for the experiment. vegetated nonagricultural areas. The forest and semi-natural areas class consists of forests, scrub and herbaceous vegetation associations, and open spaces with little or no vegetation. Water bodies include inland waters and marine waters. All these classes are situated in all training and testing sites. The classification process follows the commonly used first-level LCCS classes. For instance, in CLC nomenclature, first level thematic maps correspond to 1:100,000 and have a minimum cartographic unit (MMU) of 25 ha and geometric accuracy better than 100 m [1]. Due to the scale used in maps in land cover map production, which includes classes at this level, the images in the dataset to be used in the study were produced with the help of cartographic generalization.