Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data

Abstract

The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.

Keywords

• Machine learning
• Remote sensing
• Land use/land cover