Machine learning approaches to Landsat change detection analysis

Galen Richardson , Anders Knudby, Morgan A Crowley, Michael Sawada, Wenjun Chen

The Landsat mission has captured images of the Earth’s surface for over 50 years, and the data have enabled researchers to investigate a vast array of different change phenomena using machine learning models. Landsat-based monitoring research has been influential in geography, forestry, hydrology, ecology, agriculture, geology, and public health. When monitoring Earth's surface change using Landsat data and machine learning, it is essential to consider the implications of the size of the study area, specifics of the machine learning model, and image temporal density. We found that there are two general approaches to Landsat change detection analysis with machine learning: post-classification comparison and sequential imagery stack approaches. The two approaches have different advantages, and the design of an appropriate type of Landsat change detection analysis depends on the task at hand and the available computing resources. This review provides an overview of different approaches used to apply machine learning to Landsat change detection analysis, outlines a framework for understanding the relevant considerations, and discusses recent developments such as generative artificial intelligence, explainable machine learning, and ethical analysis considerations.