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Contributing to GCN Crop Classification

Thank you for your interest in contributing to this project! Every contribution — whether it is a bug fix, new feature, documentation improvement, or suggestion — is valued and appreciated. This guide will help you get started.

Getting Started

  1. Fork the repository on GitHub.

  2. Clone your fork locally:

    git clone https://github.com/<your-username>/GCN-Crop-Classification.git
cd GCN-Crop-Classification

  3. Create a conda environment with the required dependencies:

    conda create -n geodl python=3.9 -y
conda activate geodl
conda install pytorch torchvision torchaudio cpuonly -c pytorch -y
conda install pyg -c pyg -y
conda install rasterio geopandas scikit-learn matplotlib seaborn -c conda-forge -y
pip install -r requirements.txt

  4. Create a new branch for your work:

    git checkout -b feature/your-feature-name

Development Workflow

  1. Make your changes in your feature branch. Keep commits focused and atomic.

  2. Run flake8 linting to ensure code quality:

    flake8 . --max-line-length=120 --ignore=E501,W503,E203

  3. Test that core imports work correctly:

    python -c "from gcn_crop_classification import GCN"

  4. Commit with clear, descriptive messages:

    git add .
git commit -m "Add: short description of your change"

  5. Push your branch to your fork:

    git push origin feature/your-feature-name

Pull Request Process

  1. Update the README if your changes affect usage, installation, or project structure.
  2. Ensure CI passes — all linting and import checks must succeed before review.
  3. Use the PR template if one is provided; otherwise, include a clear summary of your changes.
  4. Request a review from a maintainer once your PR is ready.

Please be patient during the review process. Maintainers may request changes or ask clarifying questions.

Code Style

  • Follow PEP 8 conventions throughout the codebase.
  • Max line length: 120 characters.
  • Use descriptive variable names — prefer adjacency_matrix over am.
  • Add comments for complex logic, especially around graph construction and GCN layers.
  • Keep functions focused on a single responsibility.
  • Use type hints where practical.

Reporting Issues

When reporting a bug or requesting a feature, please use the appropriate GitHub issue template:

  • Bug Report: Describe the problem, steps to reproduce, expected vs. actual behavior.
  • Feature Request: Describe the proposed feature and its motivation.

In all cases, include the following environment details:

  • Operating system and version
  • Python version
  • PyTorch and PyG versions
  • CUDA version (if applicable)
  • Relevant log output or error messages

License

By contributing to this project, you agree that your contributions will be licensed under the MIT License, the same license that covers this project.