WildGuard (Poaching Detection System) is a web application designed to monitor and detect wildlife poaching activities. By leveraging the power of Google Earth Engine and a machine learning model trained on historical elephant poaching data, we provide insights into poaching hotspots. Our goal is to support conservation efforts and protect endangered wildlife species.

• Geospatial Data Integration: Uses Google Earth Engine to provide satellite imagery and geospatial data.
• Poaching Detection: Machine learning model trained on elephant poaching statistics to identify potential incidents.
• User-Friendly Interface: Built with Svelte and Bootstrap for a responsive and accessible front-end experience.
• Backend Support: Powered by Django to manage user data and handle server-side logic.

• Frontend: Svelte, Bootstrap
• Backend: Django
• Machine Learning: Yolo V5, Pytorch, TensorFlow, Jupyter Notebook
• API: Google Earth Engine API

• Python 3.8+
• Node.js
• pip (Python package manager)
• Google Earth Engine Account (Authenticated)

1. Clone the repository:

   git clone https://github.com/yourusername/wildguard.git
   cd wildguard
   

2. Set up the virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install Python dependencies:

   pip install -r requirements.txt
   

4. Install frontend dependencies:

   cd frontend
   npm install
   

5. Set up Google Earth Engine authentication: Run the following command to authenticate:

   earthengine authenticate
   

6. Apply Django mirgrations:

   cd ../backend
   python manage.py migrate
   

7. Run the application:

   • Start the Django server:

   python manage.py runserver

   • Start the Svelte frontend:

     cd ../frontend
     npm run dev

8. Access the application:

   • Open http://127.0.0.1:8000 to access WildGuard locally.

BAHA stands for:

• Britaney Do
• Aurelisa Sindhu
• Hong Le
• Aurelia Sindhu

We welcome contributions! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

• 2nd Place Winner: Won second place in the conservation-themed hackathon for wildlife protection. View on Devpost
• Successful API Integration: Integrated Google Earth Engine for satellite data analysis.
• Developed ML Model: Trained a model using a specialized elephant poaching dataset from Roboflow.

• Expand detection capabilities to other endangered species.
• Introduce real-time alerts for conservationists and authorities.
• Improve public engagement and education on wildlife protection.