Land cover classification using machine learning algorithms on GEE, and its relationship with gaseous air pollutants based on remote sensing data

Abstract

This study explores the application of machine learning algorithms on Google Earth Engine (GEE) for land cover classification and examines the relationship between different land cover types and gaseous air pollutants (O3, NO2, SO2) using remote sensing data. The research focuses on evaluating the performance of various machine learning techniques, such as Random Forest, Support Vector Machines, and CART, in classifying land cover types.