🗺️ ROADMAP

[lakillo]

📖 introduction to the project

Machine learning (ML) methods present new ways of approaching archaeological research questions and interest in applying these methods continues to grow.

This repository collects resources relating to the application of ML methods to archaeological data, aiming to:

• provide an overview of the ways ML is being applied in archaeology
• spark new ideas whilst reducing duplication of work
• encourage the sharing of code, data, and other resources
• make resources more FAIR (Findable, Accessible, Interoperable, and Reuseable)

By doing this, we hope to support practitioners to learn about, critically apply, or contribute to conversations about, ML in archaeology.

🗺️ project roadmap

The roadmap for this project is divided up into milestones: specific moments we're working towards to move the project forwards. Within milestones there will be issues that need completing to reach the milestone.

Click on the milestone names below to browse our open issues, or check out the progress of issues across all milestones on our 🚦 project kanban board.

Milestone (2): 🗺️ project vision and roadmap

• make a plan for contributor engagement channels
• make a plan for a first release and archiving
• make a plan for adding new application areas

Milestone (3): 💬 feedback on MVP

• share the repo with the community
• get feedback on usability

🌟 completed milestones

Milestone (1): 🐣 minimum viable product: completed 2024-02-09 😸

🙌 contributing

check out our ✅ contributor guidelines to find out how to contribute!

🙏 acknowledgements

This project was kicked off as part of Open Seeds cohort 8, and was inspired by these great projects: satellite-image-deep-learning, Rchaeology, open-phytoliths, AncientMetagenomeDir, and open-archaeo.