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To get started, see either of these articles.
- Register for an account on the NCI using the guide here
- Pay particular attention to the NCI Data Access Groups step of the guide. Joining these NCI projects allows you to have access to DEA's satellite datasets and derived products.
- At minimum, we recommend joining:
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wd8(to use the Virtual Desktop Intrastructure) -
xu18(to use the latest Geoscience Australia Landsat Analysis Ready Data Collection 3) -
ka08(to use the latest Geoscience Australia Sentinel-2 Analysis Ready Data Collection 3) -
jw04(to use the latest Landsat Derivatives Collection 3 data, e.g. WO, FC) -
fk4(to use legacy derived DEA products, e.g. NIDEM, ITEM, HLTC) -
if87(to use legacy Geoscience Australia Sentinel-2 Analysis Ready Data Collection 1)
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- Follow instructions here to set up the Virtual Desktop Infrastructure/VDI and install DEA
- If you run into problems with the VDI, check the latest FAQ
- Follow the instructions in Using git with DEA Notebooks to start writing your own notebooks or contribute back to the repository using
gitor Github.
When writing notebooks for your own analyses, copy and rename the DEA Notebooks template notebook. This template has been developed to make it easier to create new notebooks that meet all pull request checklist requirements. The template notebook contains a simple structure and useful general advice on writing and formatting Jupyter notebooks.
Useful links:
- The Digital Earth Australia User Guide
- The Digital Earth Australia Glossary
- Join the Open Data Cube slack. This is a friendly and active community of Open Data Cube users who are always happy to help answer any questions about DEA, Open Data Cube,
xarrayand Python! - The DEA NCI and Sandbox environments both contain slightly different datasets. To see what data is available, use:
- DEA Explorer for browsing the data that is available within DEA's NCI environment
- DEA Sandbox Explorer for browsing the data that is available within DEA's Sandbox environment
- Explore DEA products in DEA Maps
External links:
- Very extensive page of resources for performing deep learning on satellite imagery
Updating this wiki: If you notice anything incorrect or out of date in this wiki, please feel free to make an edit!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.