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Contributing to InSAR Norway Change Detection

Thank you for your interest in contributing to the InSAR Norway Change Detection project! This guide outlines how you can help improve the toolkit.

Ways to Contribute

Bug Reports

Found an issue? Help us improve by reporting bugs:

  1. Check existing issues to avoid duplicates
  2. Use the issue template when creating new reports
  3. Include system information: OS, Python version, environment details
  4. Provide reproducible examples when possible

Documentation

Help make the toolkit more accessible:

  • Fix typos or unclear instructions
  • Add examples for common use cases
  • Improve command explanations
  • Translate content (when applicable)

Code Contributions

Getting Started

Set up development environment
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# Clone the repository
git clone https://github.com/FerdinandKlingenberg/insar-norway-change-detection.git
cd insar-norway-change-detection

# Create development environment
conda env create -f environment-cpu.yml
conda activate insar-cpu-env

# Install additional development dependencies
pip install mkdocs-material[imaging]

Development Workflow

  1. Fork the repository on GitHub
  2. Create a feature branch: git checkout -b feature/your-feature-name
  3. Make your changes following the coding standards
  4. Test your changes using the existing test suite
  5. Update documentation if needed
  6. Submit a pull request with a clear description

Coding Standards

  • Follow PEP 8 for Python code style
  • Add docstrings for new functions and classes
  • Include type hints where appropriate
  • Write tests for new functionality
  • Keep functions focused and well-documented
Example function with proper documentation
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def detect_changes(data: pd.DataFrame, threshold: float = 2.0) -> pd.DataFrame:
    """
    Detect significant changes in InSAR time series data.

    Args:
        data: DataFrame containing InSAR measurements
        threshold: Statistical threshold for change detection

    Returns:
        DataFrame with detected changes and confidence scores
    """
    # Implementation here
    pass

Testing

Help ensure reliability:

  • Run existing tests: Tests are executed via GitHub Actions
  • Add test cases for new features
  • Test on different systems if possible
  • Validate with real data when contributing algorithms

Algorithm Improvements

Contribute to the change detection algorithms:

  • Optimize performance for large datasets
  • Improve accuracy of detection methods
  • Add new algorithms with proper documentation
  • Enhance spatial analysis capabilities

Documentation Contributions

Local Documentation Development

Work on documentation locally
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# Start the documentation server
mkdocs serve

# Build the documentation
mkdocs build

Visit http://127.0.0.1:8000 to preview changes.

Documentation Structure

Text Only
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docs/
├── index.md                 # Homepage
├── workflow.md             # Process overview  
├── getting-started/        # Installation & quick start
├── user-guide/            # Detailed usage guides
├── advanced/              # GPU processing & API reference
└── images/                # Screenshots and diagrams

Pull Request Guidelines

Before Submitting

  • [ ] Test your changes locally
  • [ ] Update documentation if needed
  • [ ] Follow coding standards
  • [ ] Write clear commit messages

Pull Request Template

PR Description Template
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## Description
Brief description of the changes

## Type of Change
- [ ] Bug fix
- [ ] New feature  
- [ ] Documentation update
- [ ] Performance improvement

## Testing
- [ ] Tests pass locally
- [ ] Documentation builds correctly
- [ ] Changes tested with real data (if applicable)

## Checklist
- [ ] Code follows project style guidelines
- [ ] Self-review completed
- [ ] Documentation updated

Getting Help

Communication Channels

  • GitHub Issues: Bug reports and feature requests
  • GitHub Discussions: General questions and ideas
  • Pull Request Reviews: Code-specific feedback

Development Resources

Recognition

All contributors are recognized in our project documentation. Significant contributions may be highlighted in release notes.

Ready to contribute? Check out our open issues for good first contributions!