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STAC Hackathon
Thanks for joining our two-day hackathon to help improve Digital Earth Australia’s (DEA) Notebooks!
The primary goal of the hackathon is to transition DEA Notebooks to use odc-stac. This will be the first full repository refresh of DEA Notebooks since 2019. A more detailed list of hackathon goals is outlined below.
- Convert DEA Notebooks repo to use
odc-stac.loadrather thandatacube.load - Develop skills and knowledge on
odc-stac(and STAC more generally) - Further test, refine, and document alternate access patterns for DEA (e.g. CodeSpaces, Colab)
- Develop communications and KH articles on the update to DEA Notebooks
- Address backlog DEA Notebooks tasks (‘Issues’)
- Add/update documentation and guides to the DEA Notebooks wiki on accessing DEA data via sandbox, GitHub Codespaces, Google Colab and local environments
Day 1
9am
- Introduction
- Assign yourself (or small team) to tasks in the Hackathon project board
- Get started!
Day 2
9am
- Open discussion on any issues encountered on Day 1
3.30pm
- Showcase of work
- Discuss next steps and timeline for approval and deployment of updated DEA-Notebooks
- Publication of JOSS paper timeline?
- Hackathon project board: See what needs doing, and what others are working on
- Prioritised notebook list: Shows which notebooks are highest priority for updating (but feel free to pick any!)
- Set up instructions: How to get your environment running - see below
-
develop_stacbranch: The branch we're working in - make sure you're on it! - DEA_notebooks_template: The latest notebook layout and style we are standardising on
- Introduction to loading data with STAC
- Introduction to DEA Surface Reflectance (Landsat, Collection 3)
- Using "load_ard" to load and cloud mask Landsat and Sentinel-2
- Generating satellite imagery animations
- Monitoring coastal erosion along Australia's coastline
- Knowledge Hub article on STAC
- DEAfrica
odc-stacconversion repository - FrontierSI
odc-stacexample notebooks odc-stacdocs pagepystac-clientdocs pagepystacdocs page- STAC spec
- Introduction to STAC (with the Brazilian DataCube)
- Notebook updated to follow updated DEA Notebooks template format
- For STAC loading:
- odc-stac
load()andconfigure_s3_accessimported -
pystac_clientClientimported - Environment setup includes
catalog,stac_clientandconfigure_s3_access() - Cell that searched a collection for items with
pystac-client - Cell that loads discovered items into an xarray with
odc-stac
- odc-stac
- Notebook no longer requires
datacube - Notebook no longer does
sys.path.insert(1, '../Tools/')
- Start up an "unstable" Sandbox server
- Navigate to where you have cloned your repo, e.g.:
cd dev/dea-notebooks - Make sure you have the latest copy of the repo:
git pull - Check out
develop_stacbranch:git checkout --track origin/develop_stac - Create a copy of
develop_stacto do your work:git checkout -b my_new_branch develop_stac - Install latest version of DEA Tools:
pip install -e .
Important
This step is important to make sure you can access any newly updated functions in DEA Tools.
- Update your notebook to use STAC and the new DEA Notebooks template
- When ready, commit and push your changes:
git commit -am 'I made some changes woooo' git push - Raise a pull request in the DEA Notebooks repository, setting the "base branch" as
develop_stac:
TBA!

Updating this wiki: If you notice anything incorrect or out of date in this wiki, please feel free to make an edit!
License: All code in this repository is licensed under the Apache License, Version 2.0. Digital Earth Australia data is licensed under the Creative Commons by Attribution 4.0 license.
Contact: If you need assistance with any of the Jupyter Notebooks or Python code in this repository, please post a question on the Open Data Cube Discord chat or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with any notebook, you can file one on Github.