Performance evaluation of Machine Learning algorithms for LULC classification: A case study of Fez-Meknes region

Abstract

Significant advancements have been made in remote sensing technologies, with increasingly refined applications in creating LULC maps. The integration of Machine Learning-based approaches has been explored to develop LULC maps with varying levels of precision, leveraging diverse satellite imagery. However, the task of producing LULC maps for extensive areas like the Fez-Meknes region, covering an area of approximately 40,075 km2, can be challenging using traditional methodologies. Thus, this study prioritized the major objective of establishing a reference for extracting LULC information. This endeavour involves the comparative assessment of the performance of different LULC classification approaches: Recursive Partitioning (Rpart), k-nearest neighbors (KNN), random forest (RF), Linear Discriminant Analysis (LDA), support vector machine (SVM), and extreme gradient boosting (XGBoost). For map production, remote sensing data and a supervised classification algorithm based on LANDSAT images of the Fez-Meknes region were employed. The accuracy of the generated maps was assessed using overall accuracy and Kappa coefficient. This methodology holds the potential to be replicated in other regions, utilizing a variety of available remote sensing satellite images to generate LULC maps. Essentially, the approach proposed in this study will be a valuable tool for planners, facilitating the acquisition of LULC maps at various time intervals. This will facilitate the classification of land cover types in a faster and more cost-effective manner.

Keywords

• lulc
• machine learning
• fez-meknes
• remote sensing