https://github.com/virtufirm/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/virtufirm/land_classification_model
ai classification cnn data-science deep-learning depi image-classification landcover-classification model project
Last synced: about 1 year 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/virtufirm/land_classification_model
- Owner: VirtuFirm
- License: mit
- Created: 2024-12-07T00:56:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-29T00:57:16.000Z (about 1 year ago)
- Last Synced: 2025-03-29T01:32:56.461Z (about 1 year ago)
- Topics: ai, classification, cnn, data-science, deep-learning, depi, image-classification, landcover-classification, model, project
- Language: CSS
- Homepage:
- Size: 38.6 MB
- Stars: 1
- Watchers: 2
- 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).