Land Classification Using Satellite Imagery

Using information from remote sensing, the process of identifying different land cover types—such as plants, water bodies, and soil—is known as land cover classification. This is useful for anticipating future interactions between human activity and the environment as well as for discovering significant facts about the surface of the Earth. These expected interactions lead to the formation of sustainable land use practices and the preservation of natural resources. This study focuses on classifying land cover using both supervised and unsupervised methods. The supervised method for classification uses random forest classifiers, while the supervised approach for land cover detection uses CNN. The values of the evaluation parameters are calculated and compared for the input and output photographs