"Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study" by K.S. Rawat, S. Kumar and N. Garg

The JWMM continues to publish thought provoking papers. Visit www.chijournal.org to read our latest publication "Statistical comparison of simple and machine learning based land use and land cover classification algorithms: A case study" by K.S. Rawat, S. Kumar and N. Garg. This study used three different classification models, namely Support Vector Machine (SVM), Random Forest Machine (RFM), and Maximum Likelihood (ML) for classification of Landsat (7 & 8), and Sentinel-2A data sets. It was found that urbanization influences agriculture, green cover, and water bodies, while continuously increasing built-up lands from 2005 to 2020.