Benchmarking machine learning classifiers for urban mapping in arid environments: a google earth engine analysis of Riyadh’s expansion (1990–2025)
Amal Abdelsattar
Monitoring urban expansion in arid regions is complicated by the spectral similarity between impervious surfaces and bare soil. Although machine learning classifiers on platforms like Google Earth Engine (GEE) offer effective solutions, their performance in these environments has not been systematically benchmarked. This study addresses this gap by comparing five supervised ML algorithms—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Classification and Regression