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Google Earth Engine workshop – payment and waiting list transfer

Introduction to Google Earth Engine Mini-course

Course details:

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:

This course has transitioned to an online version due to COVID-19 related precautions.  You can use your own computer, but 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 Chair_Geography@carleton.ca to discuss options.

Registration:

If you have been directed to this page because you were on the waiting list and a spot has opened up, please fill in the form below completely, and then proceed to the second page for payment.

If you were sent this link so that you could pay for this course for a colleague at work who has already registered, please fill in the form below with their name and I’m afraid you’ll have to put in SOMETHING for any of the required fields (red asterisk to the right of the question), but you do not need to worry about the details, because they have already sent us the needed information.  You will then proceed to the second screen where you can put in the payment information.

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