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One of the most difficult challenges in computer vision is image segmentation. It's the process of breaking down visual data into segments to facilitate image analysis. There are a variety of image segmentation techniques. Some examples include edge detection-based methods, region based methods, cluster based methods, partial differential equation based method, waterhed based method, and neural network based method. Image segmentation and classification is the focus of this research. Remote sensing image analysis is used to observe the Earth's surface using images taken by satellites. The primary purpose of an image classification-based system is to attribute semantic labels to the captured images. These semantic labels can then be used to classify the images in a semantic order. The suggested method uses satellite photos as its input. Deep learning (DL), a rapidly developing strong tool that surpasses these current classification approaches and gives more accurate categorization, has made it possible to achieve state-of-the-art outcomes in many remote sensing applications. The K-Means clustering algorithm can be employed to segment the remote sensing data in the current instance. This clustering method yields higher quality outcomes than conventional approaches. [ABSTRACT FROM AUTHOR]