Exploring Diverse AI Models for Enhanced Land Use and Land Cover Classification in the Nile Delta, Egypt Using Sentinel-Based Data

This study investigated Land Use and Land Cover (LULC) classification east of the Nile Delta, Egypt, using Sentinel-2 bands, spectral indices, and Sentinel-1 data. The aim was to enhance agricultural planning and decision-making by providing timely and accurate information, addressing limitations of manual data collection. Several Machine Learning (ML) and Deep Learning (DL) models were trained and tested using distinct temporal datasets to ensure model independence. Ground truth annotations, validated against a reference Google satellite map, supported training and evaluation. XGBoost achieved the highest overall accuracy (94.4%), surpassing the Support Vector Classifier (84.3%), while Random Forest produced the most accurate map with independent data. Combining Sentinel-1 and Sentinel-2 data improved accuracy by approximately 10%. Strong performance was observed across Recall, Precision, and F1-Score metrics, particularly for urban and aquaculture classes. Uniform Manifold Approximation and Projection (UMAP) technique effectively visualized data distribution, though complete class separation was not achieved. Despite their small size, road area predictions were reliable. This research highlights the potential of integrating multi-sensor data with advanced algorithms for improved LULC classification and emphasizes the need for enhanced ground truth data in future studies.