 Land cover classification of Landsat images has been a significant development in Earth observation satellites over the past four decades. This paper reviews the advancements in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images. The development of land cover classification methods grew alongside the launches of new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s, with advancements in specific classifiers and algorithms occurring in the last decade. The first methods of land cover classification applied to Landsat images were visual analyses, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means, and Isodap classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, OBIA, and hybrid approaches became common in land cover classification. The best classification results with Landsat images require attention to the specifications of each classification method, including selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier, and using suitable Landsat images. All classification methods have strengths and limitations, with OBIA being superior on different landscapes such as agricultural areas, forests, urban settlements, and wetlands. However, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over- or under-segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers, which are considered more complex methods for land cover classification. This article was authored by Darius Firei and Justin Morganroth. We are article.tv, links in the description below.