🛰️ EuroSAT Satellite Image Land Use Classification 🌍📡
EuroSAT-Satellite-Image-Land-Use-Classification is a computer vision project that leverages Deep Learning and CNN architectures to classify satellite images into different land use and land cover categories. Built on the EuroSAT dataset (based on Sentinel-2 satellite imagery), this project showcases how AI can support environmental monitoring, agriculture, and urban planning through automated satellite image classification.
✨ Key Features
🌍 Land Use Classification: Classify satellite images into 10 categories (e.g., Residential, Industrial, Pasture, Forest, River, Sea/Lake, etc.)
🖼️ High-Resolution Satellite Data: Uses the EuroSAT dataset with 27,000+ labeled images
🧠 Deep CNN Models: Custom CNNs + pretrained architectures (ResNet, VGG16, DenseNet, EfficientNet)
🧹 Preprocessing: Normalization, resizing, and augmentation for improved accuracy
📊 Evaluation Metrics: Accuracy, Precision, Recall, F1-score, Confusion Matrix
📈 Visualization: Training curves, classification reports, Grad-CAM heatmaps for model interpretability
🌐 Deployment Ready: Flask/Streamlit-based web app for uploading satellite images and real-time classification
🧰 Tech Stack
Programming: Python 🐍
Deep Learning: TensorFlow / Keras or PyTorch
Libraries: NumPy, Pandas, OpenCV, Matplotlib, Seaborn, Scikit-learn
Deployment (Optional): Flask, Streamlit, FastAPI
📁 Project Structure 📁 dataset/ # EuroSAT dataset (RGB bands) 📁 preprocessing/ # Data cleaning & augmentation scripts 📁 models/ # Deep CNN & pretrained architectures 📁 notebooks/ # Jupyter notebooks for experiments 📁 results/ # Metrics, confusion matrix, Grad-CAM visualizations 📁 app/ # Web app for image upload & prediction
🚀 Getting Started git clone https://github.com/yourusername/EuroSAT-Satellite-Image-Land-Use-Classification.git cd EuroSAT-Satellite-Image-Land-Use-Classification pip install -r requirements.txt jupyter notebook
📌 Use Cases
🌱 Agriculture: Crop and farmland monitoring
🏙️ Urban Planning: Detect residential, industrial, and commercial land use
🌳 Environmental Monitoring: Forest cover and deforestation analysis
🌊 Water Resource Management: Identification of rivers, lakes, and sea regions
📡 Remote Sensing Research: Benchmark for applying AI to geospatial data
🤝 Contributing
Contributions are welcome! You can extend the project by adding new model architectures, improving accuracy, or integrating with geospatial applications.
📜 License
MIT License – Free to use for research, education, and open-source collaboration.
⭐ Support
If you find this project useful, please consider giving it a star ⭐ to support AI in remote sensing & Earth observation.