Rewilding Project

This project is a web application that generates a random ecosystem on a grid, featuring trees, water, and animals. It's built using Svelte for the frontend and Flask for the backend. Although we couldn't implement all the features in time, it was a very fulfilling project! This is submitted for the Eaton Sustainability for AI track.

Features

• Random generation of trees, water, and animals on a grid
• Frontend built with Svelte
• Backend API using Flask
• BMP file integration (planned feature)

Project Structure

rewilding-project/
├── frontend/
│   ├── src/
│   │   ├── App.svelte
│   │   ├── app.css
│   │   └── ...
│   ├── public/
│   └── package.json
├── backend/
│   ├── process.py
│   ├── firsttake.bmp
│   └── ...
└── README.md

Setup and Installation

Frontend (Svelte)

1. Navigate to the frontend directory
2. Install dependencies: npm install
3. Run the development server: npm run dev

Backend (Flask)

1. Navigate to the backend directory
2. Create a virtual environment: python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`
3. Install dependencies: pip install -r requirements.txt
4. Run the Flask server: flask run

Usage

Open your browser and navigate to http://localhost:5000 to view the flask application, and http://localhost:5173. When opening the frontend, the grid will be populated with randomly generated trees, water, and animals.

Planned Features

• Import ecosystem recommendations from a BMP file sent from the backend
• Export the current map state as a BMP file to the backend

Scoring Functions: Carbon Capture(kg) = .5kg per pound of tree and bush 1 bmp pixel = 3cm^2 Tree weight(Spruce) = 1.9kg per 3cm^2 Bush weight(Hemlock) = 1.6kg per 3cm^2 Biodiversity= Difference from 4open:1water:1 shaded ratio Bush Count= (Biodiversity score-.2)*Open Space Available

Built With

• amazon-web-services
• flask
• pytorch
• svelte