📜 Introduction

Welcome to the Ultralytics open-source Earth observation repository! This space showcases innovative software developed by Ultralytics, demonstrating the power of Machine Learning (ML) in geospatial analysis and Earth observation. Explore our range of cutting-edge projects on the Ultralytics website.

🌍 Project Description

The Ultralytics Magellan Project pioneers the integration of ML with Earth observation data. This project enables users to visualize and interact with ML-derived insights from geospatial data directly on platforms like Google Maps and WebGL Earth, adding a dynamic dimension to data visualization and analysis in computer vision.

• Visualize on Google Maps: View Google Maps Visualization
• Explore on WebGL Earth: Interact with WebGL Earth

🛠️ Requirements

To leverage the full capabilities of the Magellan project, ensure you have the following prerequisites:

• MATLAB: Version 2018a or newer. Visit the official MATLAB Software page for installation details and support.
• Supporting Utilities: Clone the common functions repository using $ git clone https://github.com/ultralytics/functions-matlab. After cloning, add it to your MATLAB path with >> addpath(genpath('/path/to/functions-matlab')), replacing /path/to/ with the actual directory.
• MATLAB Toolboxes: Install the Statistics and Machine Learning Toolbox and the Signal Processing Toolbox. These are essential for the project's data analysis and processing tasks.

🚀 Getting Started

Follow these steps to get started with the Magellan software:

1. Set up Environment: Ensure MATLAB and the required toolboxes are installed and the functions-matlab repository is added to your path as described in the Requirements section.

2. Run Example Code: Use the following MATLAB code snippet as a starting point. Add your specific code and comments to tailor it to your analysis needs.

   % Example MATLAB code for Magellan project
   % Load your geospatial data
   % data = load('your_data.mat');
   
   % Preprocess the data if necessary
   % processed_data = preprocess(data);
   
   % Apply Machine Learning model (ensure model is trained or loaded)
   % results = predict(model, processed_data);
   
   % Visualize results using Magellan functions
   % visualize_on_map(results);
   
   disp('Magellan analysis complete. Check visualization output.');

3. Advanced Usage: For custom configurations or more complex scenarios, please refer to the specific function documentation within the repository or contact us for detailed guidance.

🖼️ Visualization Preview

Below is a preview of the kind of visualizations you can create with the Magellan project, showcasing ML insights overlaid on geographical maps.

🤝 Contribute

We thrive on community engagement! Your contributions help make Ultralytics open-source projects like Magellan even better. Check out our Contributing Guide to learn how you can get involved. We also value your feedback—please take a moment to fill out our Survey. Thank you 🙏 to everyone who contributes!

©️ License

Ultralytics provides two licensing options to suit different needs:

• AGPL-3.0 License: An OSI-approved open-source license ideal for students, researchers, and enthusiasts. It encourages open collaboration and sharing of knowledge. See the LICENSE file for full details.
• Enterprise License: Tailored for commercial applications, this license allows for the integration of Ultralytics software and AI models into commercial products and services without the open-source obligations of AGPL-3.0. For commercial use cases, please contact us via Ultralytics Licensing.

📬 Contact

Have questions, bug reports, or feature requests? We're here to help:

• GitHub Issues: For reporting bugs and requesting features, please visit GitHub Issues.
• Discord Community: Join our vibrant Discord server for discussions, support, and interaction with the Ultralytics team and other users.