DRI-EDIA-F4A

DRI-EDIA Project: Advancing Equity in Forestry: Digital Research Infrastructure and Deep Learning for All

News

• :boom: Nov., 2024: we are recruiting members of our working group at Forestry UBC. Contact: yuwei.cao@ubc.ca

  

Vision and Mission

Forestry professionals, environmental researchers, and policy makers are working together to advance digital research infrastructure and deep learning for all, and have gained significant skills to strengthen and disseminate their work in forestry research and applications through advanced computing and open science principles.

Goals

• Create clear open documents and organize workshops for people to access DRI resources in the forestry community: Remote sensing data management in DRI Model training in DRI
• Create actionable paths to contribute to open science practice with their research as case studies

Outcomes:

1. Three workshops and peer-to-peer training sessions.
2. Deep learning models as case studies in workshops, and adhering to open science principles, we provide open access to our data, code, and pre-trained models.
3. Github guideline documents / social media articles to democratize access to advanced computing and AI techniques for forestry professionals.

Repo Structure

Inspired by Cookie Cutter Data Science.

├── LICENSE
├── README.md          <- The top-level README for users of this project.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│   └── notes          <- Generated notes/records to be used in reports/meetings/workshops
|   └── presentations  <- presentations used in workshops
│
├── src                <- Source code for use in this project.
│   │── data
│   |   ├── processed      <- The final, canonical data sets for modeling.
│   |   └── raw            <- The original, immutable data dump.
|   |
│   ├── dataset           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
|   |   └── tune_model.py
|   |
│   │── checkpoints             <- Trained and serialized models, model predictions, or model summaries
│   |
│   └── visualization  <- Scripts to create exploratory and results-oriented visualizations
│       └── visualize.py
└──

🎯 Roadmap & Milestones

Roadmap & Milestones

The Team ✨

• Members: Yuwei Cao & Brianne Boufford & Harry Seely & Jing Jiang & Sarah Smith-Tripp & Tommaso Trotto & Alejandro Coca-Castro
• Roles & Responsibilities: Team Directory outlines roles, responsibilities and their ways of working.

♻️ License

This work is licensed under the MIT license. You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use and with no additional restrictions.

🤝 Citing & Acknowledgement

• Acknowledgment: Thanks Alejandro Coca-Castro for mentoring this project and his valuable suggestions!

📫 Contact

• Reach Out: Contact details for questions, feedback, or ideas.

This repository has been created for anyone to reuse. This project follows the all-contributors specification. Contributions of any kind welcome!

🤝 Credits

• These Contributing Guidelines have been adapted from the Contributing Guidelines of STEMM Role Models App! (License: MIT)