Africa Regional Data Cube Webinar - November 30, 2018
A webinar hosted by the Africa Open Data Network (AODN)
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What it can it do, and how to do it:
The ODC Sandbox
What can it do
A demonstration Data Cube Sandbox is available as an entry point to getting started with the Open Data Cube. The Sandbox will be made available here when it is openly accessible. This trial environment comes pre-packaged with the Data Cube Applications Library, a set of standard remote sensing algorithms that can be applied to any region within the global Landsat 8 data coverage, to investigate the flexibility and power of the ODC platform. The Sandbox is externally hosted and serves as a demonstration platform prior to a user obtaining their own Data Cube install.
Each user on the Sandbox has a private space to explore and modify the preloaded algorithms, however the example notebook folder is transient, and reverted to its initial state each login. It is possible to save your work to a new folder, or to download anything you develop. Users can access the Sandbox using their GitHub or Google credentials as an Open Authorization login.
How to do it
The ODC Sandbox
For those looking to engage with the Open Data Cube immediately, but don't know where to start, the ODC Sandbox provides an ideal way to get hands on. The Sandbox is an externally managed Data Cube, with global data and ARD already indexed and available to interact with. Simply access your personal Sandbox with authorization from an existing Google or GitHub account, and run any of the Data Cube Applications immediately, or create your own.
The video below shows a demonstration of accessing the Sandbox, navigating to the preloaded DCAL algorithms, and calculating NDVI over an area. The process is very similar for the other algorithms currently available in the DCAL.
DCAL and Jupyter Notebooks
The Data Cube core program can be accessed using the Python API. Python is one of the leading scientific programming languages, and one of the easiest and powerful ways to interact with Python is with the use of Jupyter Notebooks. The Jupyter environment is browser based, hosted either on your local machine or over the internet. Notebooks can be shared and modified easily, and contain space to provide detailed, customisable documentation, including web links and videos.
Data Cube Applications are made easy to interact with as interactive Widgets, which means no programming knowledge is required. Simply load the widgets, then interact with them using your mouse. Of course, the real power of the Open Data Cube comes from being able to develop your own applications and use cases.
The Data Cube Applications Library is made available on the Sandbox and Cube in a Box install as a folder of Jupyter notebooks. They are also available for download on the Open Data Cube GitHub. Code and documentation are entered in cells, which can be executed using Control+Enter, Shift+Enter, or by clicking the run button on the toolbar.
The next step
Cube in a Box
When you are looking to install a Data Cube on your own resources, a distributable, ready to run reference install is available as the “ODC Reference Install”, or Cube in a Box (CIAB). Where the Sandbox install provides an accessible, externally managed platform to trial the features of the Open Data Cube, the CIAB enables you to add your own data, be it commercial, in-situ, or derived products.