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Google Earth Engine Mini-Course

Introduction to Google Earth Engine Mini-course

Thank you for your strong interest in this course.  Registration attempts in the week leading up to Easter have resolved in errors at the payment stage – we are investigating this and will get back to you after Easter when we can get answers from the credit card people.  In the meantime, registrations are suspended, because we have reached capacity in the course.  We have created a waiting list.  If anyone else decides to cancel their registration, we will offer spots to people on the waiting list.

COVID-19 UPDATE 3:  This course will not run as originally planned.  It is being changed into an online course for remote participation, and the dates have changed. We will update this page as we work out the details, and full fee refunds will remain available. 

Google Earth Engine is a free, cloud-based application that enables time series and big data analysis of remotely sensed imagery without specialized software or the requirement to download, store and process data locally.  Through either a Javascript or Python API, Earth Engine enables programmatic access to the MODIS, Landsat 4,5,7 and 8 and Sentinel-1, 2, 3 and 5 archives, with continual updates, as well as many other imagery and ancillary datasets (e.g. land-use data, climate and soil data, etc.).  This short-course will introduce the basics of scripting in Earth Engine to enable wide-area and time series analysis of multi-spectral and SAR imagery.  Hands-on exercises in the Javascript API will be accompanied by practical demonstrations and lectures on both technical and theoretical aspects.  Scientists/analysts who have moderate resolution, large-scale or time series mapping needs will learn the building-blocks required to transfer projects into the cloud.

This course would be most useful for scientists and analysts who have a background in GIS or remote sensing and who want to integrate cloud-based remote sensing into their workflows.  Students and recent grads with GIS/Remote Sensing skills are also welcome.  Participants should be willing to learn some basic JavaScript coding skills.  While no previous coding skills are required, knowledge of scripting in other languages would be helpful (e.g. R, python etc).  Prior to the course, students will be expected to spend approximately one day setting up their Earth Engine account and reviewing some provided introductory material.

Specific topics will include:

  • viewing, sorting, filtering image collections
  • uploading tabular and image assets
  • calculating indices and texture measures
  • creating cloud masks and thresholding
  • working with reducers to generalize and summarize data
  • creating composites and mosaics (e.g. “best available pixel”)
  • using machine learning classifiers for image classification
  • exporting results (images and tables) to Google Drive

Software Requirements:

Windows machines are available in the teaching lab but students can also bring their own laptops. If bringing your own laptop, you will require: the Chrome Browser installed; Google Account including Earth Engine access.  You also might want a GIS or remote sensing software installed for local data viewing etc. (e.g. QGIS is free).

Need more information?

If you have further questions about the course, please email Koreen Millard, the course instructor.  If you are ready to register, please use the form below.  Please note that there are two stages to this registration – you need to fill out the information below and then complete the following payment form to completely enrol in the course.  If you would like to hold a spot in the course but need to get authorization from an employer prior to submitting payment, please contact to discuss options.


Sorry, this course has reached capacity.  Thank you for your interest; we hope to offer additional offerings of this and other courses as we adapt to the COVID-19 challenges.

Click here if you would like to add your name to a waiting list.  We will use the waiting list on a first-come, first-served basis in case of cancelled registrations, and to help gauge demand for future offerings of this course.