SMOTE-Enhanced Classification of Land Cover and Land use Patterns: A Comparative Evaluation

This study uses the Machine Learning Algorithms and satellite images to provide a thorough analysis of land use and land cover changes (LULC) in Ethiopia. Using Landsat imagery for classification, the study looks at the years 2015, 2017, 2019, 2021, and 2023. Data preparation, image processing, classification, and accuracy evaluation are all part of the methodology. The outcomes show how well the method works to find LULC, offering insightful information for environmental monitoring and land management. This research adds to the growing corpus of information on land cover dynamics in Ethiopia region by providing insightful information on the causes, patterns, and effects of land cover changes. By combining cutting-edge analytical methods with Landsat imagery , we can better comprehend the complex interactions between human activity and the environment, enabling stakeholders to make decisions that will support Ethiopia's natural resource conservation and sustainable development.