Youssef20015/GlobeClass-Land-Type-Classification-in-Egypt-Sentinel2-DEPI-

Land type classification in Egypt using Sentinel-2 satellite imagery and deep learning. # 🌍 GlobeClass # Land-Type Classification in Egypt using Sentinel-2 Satellite Imagery A deep learning project for **land cover classification in Egypt** using **Sentinel-2 satellite imagery**. This project leverages computer vision and deep learning models to automatically classify different land types from satellite images, supporting environmental monitoring, urban planning, and agricultural analysis. --- ## 📌 Project Overview This project aims to classify land types in Egypt from Sentinel-2 satellite images using state-of-the-art deep learning techniques. The workflow includes: - Collecting Sentinel-2 satellite imagery - Data preprocessing and augmentation - Training deep learning models - Evaluating model performance - Predicting land cover classes on unseen images --- ## 🚀 Features - 🛰️ Sentinel-2 satellite image classification - 🌍 Land cover detection in Egypt - 🧠 Deep learning-based image classification - 📊 Model evaluation and performance metrics - 📈 Training visualization (accuracy & loss) - 🔍 Prediction on custom satellite images --- ## 🛠️ Technologies Used - Python - PyTorch - NumPy - Pandas - Matplotlib - Scikit-learn - OpenCV - Pillow (PIL) - Jupyter Notebook --- ## 📂 Project Structure ``` Land-Type-Classification-in-Egypt-Sentinel2-DEPI/ │ ├── dataset/ ├── notebooks/ ├── models/ ├── outputs/ ├── images/ ├── train.py ├── predict.py ├── requirements.txt └── README.md ``` --- ## 📊 Dataset This project uses **Sentinel-2 satellite imagery** for land cover classification. Example land types include: - 🌾 Agricultural Land - 🌲 Forest - 🏜️ Desert - 🏙️ Urban Area - 🌊 Water Bodies - 🌱 Vegetation - 🛣️ Roads - 🏭 Industrial Areas --- ## ⚙️ Installation Clone the repository: ```bash git clone github.com cd Land-Type-Classification-in-Egypt-Sentinel2-DEPI ``` Install dependencies: ```bash pip install -r requirements.txt ``` --- ## ▶️ Training Run: ```bash python …