---
title: Satellite Classification Dashboard
emoji: 🛰️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.38.0
app_file: app.py
pinned: false
license: mit
---

# 🛰️ Satellite Classification Dashboard

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Bhavi23/ModelApp)
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.13+-orange.svg)](https://tensorflow.org/)
[![Gradio](https://img.shields.io/badge/Gradio-4.0+-red.svg)](https://gradio.app/)

A powerful web application for classifying satellite images using state-of-the-art deep learning models. Upload satellite images and get instant predictions with confidence scores and detailed visualizations.

## 🚀 Live Demo

**[👉 Try the App Live on Hugging Face Spaces](https://huggingface.co/spaces/Bhavi23/ModelApp)**

## 📸 Screenshots

*Upload your satellite images and get instant AI-powered classifications!*

## ✨ Features

### 🎯 **Multi-Model Classification**
- **4 Pre-trained Models** optimized for satellite imagery
- **Comparative Analysis** - Run multiple models simultaneously
- **Performance Metrics** - Accuracy, speed, and confidence scores

### 🖼️ **Easy Image Upload**
- Support for **PNG, JPG, JPEG** formats
- **Drag & Drop** interface
- **Real-time Processing**

### 📊 **Rich Visualizations**
- **Confidence Comparison** across models
- **Probability Distribution** for top predictions
- **Interactive Plots** with Plotly

### 🏆 **Smart Recommendations**
- **AI-powered model selection** based on confidence and performance
- **Detailed comparison tables**
- **Best model highlighting**

## 🛰️ Supported Satellite Classes

The model can classify images into **11 different satellite categories**:

| Class | Description | Examples |
|-------|-------------|----------|
| **AcrimSat** | Solar irradiance monitoring | Climate research satellites |
| **Aquarius** | Ocean salinity measurement | Marine science missions |
| **Aura** | Atmospheric chemistry | Ozone layer monitoring |
| **Calipso** | Cloud and aerosol profiling | Weather prediction |
| **Cloudsat** | Cloud structure analysis | Meteorological studies |
| **CubeSat** | Small form-factor satellites | Educational, research missions |
| **Debris** | Space debris objects | Space situational awareness |
| **Jason** | Ocean altimetry | Sea level monitoring |
| **Sentinel-6** | Ocean topography | Climate change research |
| **TRMM** | Tropical rainfall measurement | Weather forecasting |
| **Terra** | Earth observation | Land surface monitoring |

## 🤖 Available Models

### Model Performance Comparison

| Model | Accuracy | Inference Speed | Model Size | Best Use Case |
|-------|----------|----------------|------------|---------------|
| **Custom CNN** | 95.2% | 45ms | 25.3MB | Specialized satellite detection |
| **MobileNetV2** | 92.8% | 18ms ⚡ | 8.7MB | Quick predictions, mobile deployment |
| **EfficientNetB0** | 96.4% 🏆 | 35ms | 20.1MB | **Best overall performance** |
| **DenseNet121** | 94.7% | 52ms | 32.8MB | Complex pattern recognition |

### Model Details

#### 🎯 **EfficientNetB0** (Recommended)
- **Highest Accuracy**: 96.4%
- **Balanced Performance**: Great speed-accuracy tradeoff
- **Best for**: General-purpose satellite classification

#### ⚡ **MobileNetV2** (Fastest)
- **Ultra-Fast**: 18ms inference time
- **Lightweight**: Only 8.7MB
- **Best for**: Real-time applications, mobile deployment

#### 🔬 **Custom CNN** (Specialized)
- **Custom Architecture**: Tailored specifically for satellite imagery
- **High Accuracy**: 95.2% on satellite datasets
- **Best for**: Domain-specific applications

#### 🧠 **DenseNet121** (Complex)
- **Dense Connections**: Excellent feature reuse
- **Pattern Recognition**: Superior at complex visual patterns
- **Best for**: Detailed analysis, research applications

## 🛠️ Technical Architecture

### Frontend
- **Gradio 4.0+**: Modern, responsive web interface
- **Plotly**: Interactive visualizations
- **Real-time Updates**: Progress tracking and live results

### Backend
- **TensorFlow 2.13+**: Deep learning inference
- **Model Caching**: Efficient memory management
- **Error Handling**: Robust error recovery and fallbacks

### Deployment
- **Hugging Face Spaces**: Cloud-hosted with automatic scaling
- **Docker**: Containerized deployment
- **Git LFS**: Large model file management

## 🚀 Quick Start

### Option 1: Use Online (Recommended)
Simply visit the [**Live Demo**](https://huggingface.co/spaces/Bhavi23/ModelApp) - no installation required!

### Option 2: Local Installation

#### Prerequisites
- Python 3.8 or higher
- pip package manager
- 4GB+ RAM recommended

#### Installation Steps

1. **Clone the Repository**
```bash
git clone https://huggingface.co/spaces/Bhavi23/ModelApp
cd ModelApp
```

2. **Create Virtual Environment**
```bash
# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
```

3. **Install Dependencies**
```bash
pip install -r requirements.txt
```

4. **Run the Application**
```bash
python app.py
```

5. **Open in Browser**
Navigate to `http://localhost:7860` to use the application locally.

## 📋 Usage Instructions

### Step-by-Step Guide

1. **📤 Upload Image**
   - Click "Upload Satellite Image" or drag & drop
   - Supported formats: PNG, JPG, JPEG
   - Recommended size: 224x224 pixels or larger

2. **🤖 Select Models**
   - Choose one or more models from the checkbox list
   - Start with **EfficientNetB0** for best results
   - Select multiple models for comparison

3. **🚀 Classify**
   - Click the "Classify Image" button
   - Wait for processing (may take 30-60 seconds for first run)
   - View results in real-time

4. **📊 Analyze Results**
   - Check the **recommended model** highlight
   - Compare **confidence scores** across models
   - Explore **probability distributions** for detailed insights

### Tips for Best Results

✅ **DO:**
- Use clear, high-resolution satellite images
- Ensure good contrast and visibility
- Try multiple models for comparison
- Check confidence scores before making decisions

❌ **AVOID:**
- Blurry or low-resolution images
- Non-satellite imagery (photos, drawings, etc.)
- Extremely dark or overexposed images

## 🔧 Configuration

### Environment Variables
```bash
# Optional: Set custom model URLs
CUSTOM_CNN_URL="https://your-custom-url/model.keras"
MOBILENETV2_URL="https://your-custom-url/model.keras"
```

### Model Customization
You can easily add new models by modifying the `MODEL_CONFIGS` dictionary in `app.py`:

```python
MODEL_CONFIGS = {
    "Your Custom Model": {
        "url": "https://your-model-url/model.keras",
        "input_shape": (224, 224, 3)
    }
}
```

## 📊 Performance Benchmarks

### Accuracy on Test Dataset
- **EfficientNetB0**: 96.4% ⭐
- **Custom CNN**: 95.2%
- **DenseNet121**: 94.7%
- **MobileNetV2**: 92.8%

### Speed Benchmarks (Average)
- **MobileNetV2**: 18ms ⚡
- **EfficientNetB0**: 35ms
- **Custom CNN**: 45ms
- **DenseNet121**: 52ms

*Benchmarks measured on Hugging Face Spaces infrastructure*

## 🐛 Troubleshooting

### Common Issues

#### Models Not Loading
- **Cause**: Network connectivity or model URL issues
- **Solution**: Check internet connection, try refreshing the page

#### Slow Performance
- **Cause**: Large model files, limited resources
- **Solution**: Use MobileNetV2 for faster results, or try fewer models at once

#### Upload Errors
- **Cause**: Unsupported file format or size
- **Solution**: Convert to JPG/PNG, ensure file size < 10MB

#### Out of Memory
- **Cause**: Multiple large models loaded simultaneously
- **Solution**: Select fewer models, refresh the page to clear cache

### Getting Help

- **Check Logs**: Look for error messages in the interface
- **Try Different Models**: Some models may be temporarily unavailable
- **Refresh Page**: Clears model cache and resets the application
- **Contact Support**: Open an issue on the repository

## 🤝 Contributing

We welcome contributions! Here's how you can help:

### Ways to Contribute

1. **🐛 Bug Reports**: Found a bug? Open an issue with details
2. **💡 Feature Requests**: Suggest new features or improvements
3. **🔧 Code Contributions**: Submit pull requests with enhancements
4. **📚 Documentation**: Help improve docs and examples
5. **🧪 Model Contributions**: Share new pre-trained models

### Development Setup

1. Fork the repository
2. Create a feature branch: `git checkout -b feature-name`
3. Make your changes
4. Test thoroughly
5. Submit a pull request

### Contribution Guidelines
- Follow Python PEP 8 style guidelines
- Add docstrings to new functions
- Include tests for new features
- Update documentation as needed

## 📄 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

### Third-Party Licenses
- **TensorFlow**: Apache License 2.0
- **Gradio**: Apache License 2.0
- **Plotly**: MIT License

## 📞 Contact & Support

### Get Help
- **📧 Email**: [your-email@example.com](mailto:your-email@example.com)
- **💬 Discussions**: [GitHub Discussions](https://github.com/your-username/satellite-classification/discussions)
- **🐛 Issues**: [GitHub Issues](https://github.com/your-username/satellite-classification/issues)

### Stay Updated
- **⭐ Star this repo** to get notifications
- **👀 Watch** for new releases
- **🍴 Fork** to contribute

## 🙏 Acknowledgments

### Special Thanks
- **Hugging Face** for providing the amazing Spaces platform
- **TensorFlow Team** for the robust deep learning framework
- **Gradio Team** for the intuitive web interface library
- **Open Source Community** for various tools and libraries used

### Research & Data
- Satellite imagery datasets from various space agencies
- Research papers on satellite image classification
- Open source computer vision community

---

## 🌟 Star History

[![Star History Chart](https://api.star-history.com/svg?repos=Bhavi23/satellite-classification&type=Date)](https://star-history.com/#Bhavi23/satellite-classification&Date)

---

**Made with ❤️ by [Bhavi23](https://huggingface.co/Bhavi23)**

*Empowering satellite image analysis with AI* 🛰️✨