Overview: The cloud native geospatial paradigm has the potential to make Earth observation analysis accessible to more people, more easily. In the simplest terms, instead of downloading data before performing an analysis, it’s now possible to stream data directly from the cloud.
This workshop will explore this new capability using hands-on practical exercises, introducing participants to the incredible global datasets available online and an opinionated suite of tools that can be used to access them.
At the end of the workshop, participants will have gained insight into how this cloud-native geospatial paradigm can simplify working with Earth observation data, along with practical examples to assist in implementing learnings going forward.
The workshop will include a real-world use case documenting land productivity metrics, which are used as part of monitoring for the UN sustainable Development Goal indicators for 15.3.1. We’ll explore this metric using NASA’s Harmonized Landsat and Sentinel data accessed through Earthdata.
The workshop will be run as part of SatSummit in Washington, D.C. on the 16th and 17th of May, 2024. If you’re already attending SatSummit, please let us know you’re interested in attending the workshop by completing this form. If you'd like to attend the workshop independently of the broader event, you can purchase a Cloud Native EO Training Only ticket.
Requirements: This is an introductory workshop, but some experience with programming languages like Python will be helpful. If you have significant experience working with large volumes of Earth observation data, but haven’t worked with cloud native technologies like Cloud Optimised GeoTIFFs indexed in a Spatio-Temporal Asset Catalog, there are likely some techniques that will be covered that are useful.
We’ll provide a small hosted development environment, and an Internet connection, so you only need to bring a laptop along. If you have a way of working with Jupyter Notebooks that you like, you can run the workshop in your environment. Key Python libraries we’ll be using include Xarray, Geopandas, PySTAC Client, ODC STAC and ODC Geo.
