Prometheus

3D Light Pollution Visualization using Raylib and Earth Engine

Authors

• @0xS4cha

Result

Description

This project is a Python project focused on fetching, processing, and visualizing global light pollution data in a 3D environment. It utilizes the Google Earth Engine API to retrieve night light data and the raylib graphics library to render the data points on a 3D spherical representation of Earth. It has been created to explore 3D data visualization, spherical coordinates, and Earth Engine integration.

graph TD
    %% Styles
    classDef data fill:#BBDEFB,stroke:#1565C0,stroke-width:2px,color:black;
    classDef process fill:#C8E6C9,stroke:#2E7D32,stroke-width:2px,color:black;
    classDef graphics fill:#FFF9C4,stroke:#FBC02D,stroke-width:2px,color:black;
    classDef app fill:#E1BEE7,stroke:#6A1B9A,stroke-width:2px,color:black;

    %% Nodes
    App["PlanetApp<br/>Main Entry Point<br/>(sservant)"]:::app
    
    Data["EarthEngineDataProvider<br/>Fetch Data<br/>(sservant)"]:::data
    Processor["LightMapProcessor<br/>3D Projection<br/>(sservant)"]:::process
    Visualizer["PlanetVisualizer<br/>3D Rendering<br/>(sservant)"]:::graphics

    %% Connections
    App -->|1. Request data| Data
    App -->|2. Generate points| Processor
    App -->|3. Render planet| Visualizer
    
    Data -->|Image path| Processor
    Processor -->|3D Point Cloud| Visualizer

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Instruction

The Prometheus visualizer can be run using standard Python tools. Dependencies are managed via pyproject.toml.

pip install .

Or using uv (recommended):

uv pip install -e .

Usage

Python Script

from src.app import PlanetApp

# Initialize and run the application
app = PlanetApp()
app.run()

Command Line

Run the application directly:

python -m src

Algorithms

3D Projection & Visualization

The project implements several key components for processing and rendering geographic data:

• Data Acquisition: Interfacing with the Earth Engine API to download global night lights composite imagery.
• Spherical Projection: Converting 2D equirectangular map coordinates from the image into 3D Cartesian coordinates (x, y, z) to map the light pollution points onto a sphere.
• Point Cloud Optimization: Processing large image datasets and converting pixel intensities into scaled point sizes and colors for efficient 3D rendering.

The algorithm considers:

1. Fetching data from Earth Engine
2. Parsing and thresholding the 2D image array using pandas and matplotlib
3. Calculating spherical coordinates to map data accurately on a sphere of radius 10.0
4. Interactive 3D rendering using raylib

Project Structure

│  pyproject.toml  # Project configuration and dependencies
│  README.md       # Project documentation
└  src/
   ├── __main__.py   # Main entry point
   ├── app.py        # Core application logic
   ├── data.py       # Earth Engine data provider
   ├── processor.py  # Image to 3D point processor
   └── visualizer.py # Raylib 3D graphics engine

Resources

• Raylib — Graphics library for 3D visualization
• Google Earth Engine — Global geospatial data platform
• Spherical Coordinate System — Mathematics for 3D mapping
• Gemini — Documentation assistance

Disclaimer

⚠️ IMPORTANT — Educational use only:

• Do not copy this code for assessed work in 42 or other courses.
• Use this repository for study and reference only.

Feedback

If you have feedback, open an issue or contact the author.