Integrating Convolutional Neural Network and Multiresolution Segmentation for Land Cover and Land Use Mapping Using Satellite Imagery  
[PERSON]  
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[PERSON]  
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CLC 2006, and 1.5 years for CLC 2018 [2]. There are similarities and differences in the production techniques and standards of each product. However, many databases use visual interpretation and digitizing on-screen processes in LULC map production. USGS produces Anderson LULC data using aerial photos by visual analysis in 1:250,000 scales [3]. CLC Map of Great Britain (CLCMGB) identifies land use classes using generalization procedures. These products were created using conventional visual image interpretation techniques, besides the considerable ancillary knowledge about land use [4]. However, there have been attempts to implement semiautomatic processes in producing LULC steps. [PERSON] and [PERSON] used a semiautomatic interactive approach for wetland classes, and their method is also used for producing the Swedish CLC [5]. Such processes are becoming more efficient with improvements in technology and devices.  
Artificial intelligence (AI) presents new techniques and advantages for computer vision and image processing. In this concept, deep learning (DL) algorithm usage is increasing tremendously due to being robust and having fewer human operations. Deep learning is still a new field and has the potential to develop more over time. Therefore, land use classification of remote sensing data is experiencing tremendous growth, and the use of DL algorithms as methods has been increasing in the last 5 years [6]. In recent years, many challenges and datasets have arisen for the usage of DL in remote sensing applications. Many CNN models are used with Earth observation data: aerial photos and satellite imagery. In DL, there is a hidden feature extraction step; the algorithm learns to make an accurate prediction through its own data processing by using its artificial multilayered neural network structure and has a hidden layer. DL applications via remote sensing imagery can be grouped under object detection, classification, and segmentation.  
This study aims to produce land cover maps using the CNN-based models combining MRS application to refine the accuracies and automation of the land cover mapping process. The CNN-MRS model consists of two main steps: CNN-based land cover classification and improving the performance of the classification with spatial filter and multiresolution segmentation (MRS). The CNN-MRS model includes the U-Net and SegNet CNN algorithms. Problems such as noise encountered in the pixel-based approach are reduced by the object-based technique. Each of the segments created as a result of MRS is evaluated as an object. The weighted pixel label inside an object is spread over the entire object. The classification accuracy is increased by combining deep learning and multiresolution segmentation.