Land Use Mapping and Change Detection in the Grombalia Region Using Sentinel-2 Imagery and Machine Learning -Ashma Thamri

This study mapped land use changes in the Grombalia Region of Tunisia using Sentinel-2 imagery and machine learning. Three classifiers—Random Forest, SVM, and CNN—were compared. Random Forest achieved the highest accuracy. Results highlight agricultural changes over time and demonstrate the effectiveness of remote sensing for environmental monitoring.