Improving land cover change modeling with machine learning: comparative analysis of SVM and XGBoost in Lesotho Lowlands

The Lowlands of Lesotho are critical for socio-economic growth, hosting significant activities and infrastructure, yet they face pressures from population growth, urbanisation, and land degradation. This study assessed the performance of two leading machine learning algorithms, Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), in predicting land cover change from 1993 to 2039. The findings revealed a critical insight into model validation: while SVM consistently achieved high