## 2 Related Works  
In the past, land cover map producing method and accuracy and the data were different from current studies. The National Land Cover Database 1992 (NLCD 1992) for the United States, depending on using multi-temporal Landsat TM, used an unsupervised clustering method with the range of 74% to 85% overall accuracies in NLCD 2001 [7]. In CLC 1990, 2000, 2006, and 2012 from 50 to 25 m, geometric accuracy satellite data were used. In CLC 2018, Sentinel-2 imagery was first used, with \(\geq\)85% thematic accuracy. CLC is produced by a visual interpretation by the majority of countries [2]. In recent products, semiautomatic solutions have started to be applied by using in situ data, combining geographic information systems (GIS) with digital image processing, and generalization by a few countries. In the last decade, the deep learning approach has been effective as a robust method for land cover classification using satellite images [8]. Semantic segmentation of satellite images with CNNs has been conducted for land-cover-based environmental monitoring. There are many tasks completed in this manner, including urbanization [9] and slum areas [10], forestry and vegetation [11], heterogeneous LULC classes [12], and watershed geospatial artificial intelligence (geoAI) applications. There are some large datasets used for segmentation studies, which are also used in several contests such as Deep Globe [13] and Big Net Earth [14]. Numerous challenges are available for image classification on platforms such as Kaggle and CrowdAnalytix to increase the LULC classification accuracy. On the other hand, there are methods such as U-Net and U-CAM, where segmentation accuracies of more than 85% can be achieved using datasets of modest size [15]. [PERSON] et al. [16] proposed a method that can increase semantic segmentation classification accuracy by using multiple kernels and data fusion from heterogeneous sensors (optical and laser) using CNN architecture. In the same study, the image segments were produced with multiresolution segmentation. NDVI, homogeneity, brightness, and rectangular fit features for each segment were obtained with the help of images from Semarang, Indonesia, and were given as input to deep learning. Another semantic segmentation application in remote sensing was presented by [PERSON] et al. [17]. Multispectral imagery (MSI) was used in their study, and synthetic MSI data were used using deep convolutional neural networks (DCNNs). Obtained weights were transferred to the real very high-resolution (VHR) MSI data to perform pixel wise classification. [PERSON] et al. [18] proposed combining OBIA and DL and tested with VHR aerial remote sensing images for change detection analysis for natural disasters. In another study [19], Geo Eye-1 and Worldview-1 images were used in CNN architecture. The resulting CNN probabilities were used for object refinement using OBIA segmentation and fuzzy classification to generate sample segmentation of dwellings in a refugee camp. [PERSON] et al. used a developed object-based learning framework and compared the results with pixel-based learning using CNN in VHR imagery [20]. [PERSON] et al. presented a framework including OBIA, skeleton-based algorithm usage for object analysis, and conducting several CNNs for land cover mapping using high-spatial-resolution, and the proposed method's overall accuracy was 87.2% [21]. Another study [22] compared the results of fully convolutional networks, machine learning algorithms, and patch-based DCNN for object-based wetland mapping using unmanned aircraft system images. [PERSON] et al. used a methodology that includes an object-based CNN model for LULC mapping with high-resolution imagery [23]. [PERSON] et al. proposed a model as a region-based analysis for semantic segmentation, and they used spatial regularization and added the region level (related group of pixels) into conditional random fields (CRFs) on VHR satellite and aerial imagery [24].