The Solar-Panels-Segmentation-U-Net project is a Python notebook (.ipynb) that focuses on segmenting solar panels from aerial imagery using the U-Net architecture. It aims to assist in the identification and analysis of solar panel installations from satellite or drone images.

Solar energy is an increasingly popular and renewable energy source. The project aims to automate the process of identifying and segmenting solar panels within images, enabling efficient analysis, monitoring, and assessment of solar energy installations.

The U-Net architecture, a convolutional neural network (CNN), is employed for semantic segmentation tasks. By leveraging U-Net, the project achieves accurate solar panel segmentation by differentiating them from the surrounding structures and backgrounds.

The repository contains the following files:

• Solar_Panels_Segmentation_U_Net.ipynb: This Python notebook is the main project file. It contains the implementation of the U-Net architecture for solar panel segmentation, along with detailed explanations, code comments, and visualizations.

• data/: This directory contains sample data for testing and demonstration purposes. It includes aerial imagery with solar panels and their corresponding ground truth masks.

• results/: This directory will be automatically generated when running the notebook. It stores the segmented solar panel masks and any intermediate results or visualizations.

• README.md: The README file provides an overview of the project and instructions for running and contributing to it.

To get started with the Solar-Panels-Segmentation-U-Net project, follow these steps:

1. Clone the Repository: Start by cloning this repository to your local machine using the following command:

   git clone https://github.com/salimhammadi15/Solar-Panels-Segmentation-U-Net.git
   

2. Open the Jupyter Notebook: Launch Jupyter Notebook or any other compatible environment and navigate to the cloned repository.

3. Install Dependencies: Ensure that all required dependencies are installed. You can install them using the following command:

   pip install -r requirements.txt
   

4. Run the Notebook: Open the Solar_Panels_Segmentation_U_Net.ipynb notebook and run the cells sequentially. The notebook provides step-by-step instructions and explanations for implementing the U-Net architecture, training the model, and performing solar panel segmentation.

5. Explore Results: Once the notebook is running, you can visualize the segmented solar panel masks and explore the results. The results/ directory will contain the generated output images and any intermediate visualizations.

Contributions are welcome to enhance the Solar-Panels-Segmentation-U-Net project. To contribute, follow these steps:

1. Fork the Repository: Click on the "Fork" button at the top right corner of the repository page. This will create a copy of the repository in your GitHub account.

2. Make Changes: Create a new branch, make your desired changes in the notebook, and add any additional features or improvements.

3. Test Your Changes: Run the notebook and ensure that your modifications work correctly.

4. Commit and Push: Commit your changes and push them to your forked repository:

   git add .
   git commit -m "Your commit message"
   git push origin your-branch-name
   

5. Create a Pull Request: Go to the original repository on GitHub and click on the "New Pull Request" button. Fill out the details and submit the pull request. Your changes will be reviewed by the project maintainers.

This project is licensed under the MIT License. See the LICENSE file for more information.

Special thanks to the developers and researchers in the field of reinforcement learning for their contributions and inspiration in the implementation of the Multi-Armed Bandits problem using the Greedy Policy.