A Comparison among Different Machine Learning Algorithms in Land Cover Classification Based on the Google Earth Engine Platform: The Case Study of Hung Yen Province, Vietnam

Based on the Google Earth Engine cloud computing data platform, this study employed three algorithms including Support Vector Machine, Random Forest, and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images, a free data source with reasonable spatial and temporal resolution. The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land, Water bodies, Perennial vegetation, Annual vegetation, Built-up areas as their overall accuracy and Kappa coefficient were greater than 80% and 0.8, respectively. Among the three algorithms, SVM achieved the highest accuracy as its overall accuracy was 86% and the Kappa coefficient was 0.88. Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha, accounting for more than 33.8% of the total natural area, followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha (33%) and 15,637 ha (16.8%), respectively. Water bodies and Annual vegetation cover the smallest areas with 8,820 (9.5%) ha and 6,302 ha (6.8%), respectively. The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.