Remote sensing provides us with valuable information about the state and evolution of our environment. Processes such as soil and coastal erosion, crop growth, water quality or bushfires can be monitored through the images collected by satellites. However, these collections of satellite images are often extremely large, which presents a significant challenge to users wanting to process or store these data.

Geoscience Australia has developed a platform called Digital Earth Australia (DEA), that enables users to perform large scale satellite image analysis easily on personal computers. Using a high-performance storage and computing platform provided by the National Computational Infrastructure, DEA provides the tools to search and analyse high quality satellite image collections that span more than 30 years covering the whole Australian continent.

This course presents the tools and techniques for processing satellite imagery through practical examples.

The materials will get you familiar with using the DEA by connecting to the National Computational Infrastructure. A quick introduction to scientific Python is provided and geospatial concepts are presented using practical and interactive examples using Jupyter notebooks. By the end of the course, you will have the necessary knowledge to use satellite data and apply it to solve a wide range of analytical problems using Python.

This training focuses on understanding and using:

• NCI infrastructure (access, Linux environment, file system, data services)
• NCI Virtual Desktop Interface (VDI)
• Python and Jupyter for spatial data (Python @ VDI, introduction to scientific Python, xarray)
• DEA (examples of querying, loading, processing and exporting data)
• Geospatial analysis using DEA (examples of querying, loading, processing and exporting data)

This course is designed to introduce data analysts working in government to satellite imagery and geospatial data processing. Attendants will be introduced to the data and tools required to analyse satellite data with a focus on the application to environmental policy and decision-making problems.

The course materials are provided using the Python programming language. Although previous knowledge of Python is not required, having some experience with other programming languages is highly recommended

Duration: Training course will be delivered over 2 days (6 hours each day).

Format: Hands-on exercises and practical examples delivered to an audience of 10-20 people.

Dates and Location: 3 sessions to be held between August 2019 and March 2020.

See the Program for a complete description of the contents.

Having trouble with Pages? Check out our documentation or contact support and we’ll help you sort it out.