https://github.com/ali-el-badry/land_classification_model

This repository hosts the Land Type Classification using Sentinel-2 Satellite Images project, which focuses on leveraging state-of-the-art deep learning techniques to classify various land types from multispectral satellite imagery
https://github.com/ali-el-badry/land_classification_model

ai classification cnn data-science deep-learning depi image-classification landcover-classification model project

Last synced: about 3 hours ago
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This repository hosts the Land Type Classification using Sentinel-2 Satellite Images project, which focuses on leveraging state-of-the-art deep learning techniques to classify various land types from multispectral satellite imagery

• Host: GitHub
• URL: https://github.com/ali-el-badry/land_classification_model
• Owner: Ali-EL-Badry
• License: mit
• Created: 2024-12-07T00:56:51.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-22T18:24:32.000Z (about 2 months ago)
• Last Synced: 2024-12-22T19:18:48.645Z (about 2 months ago)
• Topics: ai, classification, cnn, data-science, deep-learning, depi, image-classification, landcover-classification, model, project
• Homepage:
• Size: 12.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 🌍 Land Type Classification Using Sentinel-2 Satellite Images

This repository hosts the **Land Type Classification using Sentinel-2 Satellite Images** project, which focuses on developing a powerful deep learning model to classify different land types using multispectral satellite imagery. By harnessing the capabilities of Sentinel-2 data and Deep Neural Networks (DNNs), the project aims to contribute to crucial fields such as:

- **Urban Planning** 🏙️  

- **Agriculture** 🌾  

- **Environmental Conservation** 🌳  

## 📌 Project Highlights

- **Multispectral Data Analysis:** Utilizing Sentinel-2 satellite imagery for precise land classification.  

- **Deep Learning:** Implementing robust DNN architectures to achieve high classification accuracy.  

- **Real-World Applications:** Supporting informed decision-making in sustainable development, resource management, and environmental monitoring.  

## 🛠️ Milestones & Approach

The project is structured into clear phases to ensure thorough data processing, model training, evaluation, and deployment. Key milestones include:  

1. **Data Collection and Preprocessing**  

2. **Model Development and Optimization**  

3. **Deployment and Usability Testing**  

## 🔮 Future Prospects

This project sets the foundation for continuous improvement and scalability, offering valuable insights for future research and applications.  

---

## 🤝 Contributors

**Alpha 5** will build innovative solutions for a sustainable future. 🌟

- Aly El-Deen Yasser Ali (Known By Aly El-Badry) -- [Linkedin](https://www.linkedin.com/in/aly-el-badry/)

- Mohammed Walid (Known by Mohammed Gafour) -- [Linkedin](https://www.linkedin.com/in/mohammadgafour/)

- Amr Yasser  -- [Linkedin](https://www.linkedin.com/in/amryasser25/)

- Dina Zahran -- [Linkedin](https://www.linkedin.com/in/dina-zahran-dz/)

- Sherin Mohamed -- [Linkedin](https://www.linkedin.com/in/sherin-mohamed-sm/)

## 📜 License  

This project is licensed under the [MIT License](LICENSE).