This short course will cover a hands-on introduction to satellite imagery, image processing, and Earth observation data analysis techniques. Through examples involving real-world data, you will learn the fundamentals of Data Science analyses and image processing, how to obtain satellite imagery and combine it with other georeferenced datasets, and how to explore research questions using satellite imagery. You will ultimately learn how to develop a software pipeline to extract, manipulate, and visualise Earth Observation (EO) imagery.

Course dates

Oct 17 - Nov 16, 2023. This includes 10 hands-on workshops: 2 x 2-hour workshops in the first two weeks, and 2 x 3-hour workshops in the final three weeks.

Course content

The workshops and online material will include explanations of key concepts, hands-on coding exercises, and applied EO data analysis projects where you can apply your new skills to real-world satellite imagery. The course will cover the following key themes:

Satellite imagery fundamentals

• Different types of satellites and associated imagery
• Fundamentals of image processing
• Satellite orbits
• Satellite imagery data structures

Pandas & GeoPandas

• Data building blocks: the Series, DataFrame, GeoSeries, and GeoDataFrame
• Cleaning, validating, and transforming your data

Data Science best practise

• Coding best practise
• Data science project organisation

Visualising satellite imagery

• Basic data visualisation in Python and Matplotlib
• Plotting different satellite bands and indices
• Combining raster and vector images
• Interactive maps with Folium

Earth observation data platforms

• Digital Earth Australia Sandbox
• Google Earth Engine
• Extracting relevant satellite imagery

Data analyses with satellite imagery

• Exploring research questions with satellite imagery
• Mapping vegetation
• Time-series analysis and change mapping
• Performing global analyses
   

Course location

The workshops will be delivered face-to-face at Swinburne University in Hawthorn, Victoria, as well as online for those that cannot make it to campus.