Google Earth Engine Tutorials

These Google Earth Engine (GEE) tutorials provide a foundation to quickly begin learning and using GEE. If you are new to GEE, you will want to start with this Google Earth Outreach tutorial. You may need to sign-up for a GEE account with an existing Gmail email address.

Set 1: Prepared by Dr. J.B. Sharma and Mr. Zachary Noah of the University of North Georgia

  • GEE_0:  The Google Earth Engine Explorer:  Training Classifiers, Supervised Classification and Error Assessment
    • How to add raster and vector data from the catalog in Google Earth Engine;
    • Train a classifier;
    • Perform the error assessment;
    • Download the results.
  • GEE_1: Google Earth Engine Tutorial Pt. IData Acquisition
    • Acquiring data stored on Google’s servers for use in Google Earth Engine.
  • GEE_2: Google Earth Engine Tutorial Pt. IIClipping
    • How to clip a raster image to the extent of a vector polygon in order to speed up processing times as well to display only the imagery you want.
  • GEE_3: Google Earth Engine Tutorial Pt. IIIVisualization
    • How to use the knowledge of how to visualize images that you learned in previous tutorials and embed the visualization parameters inside of the GEE script so that the imagery will appear with the same visualization every time it is run.
  • GEE_4: Google Earth Engine Tutorial Pt. IVPixel Selection
    • Select pixels from rasters with conditional statements and boolean operators;
    • Select from multiple bands and images to create a single selection;
    • Create new image and transfer selected pixels to new image.
  • GEE 5: Google Earth Engine Tutorial Pt. V: Raster Algebra
    • Select bands from multispectral images
    • Use mathematical operators to perform raster algebra
    • Calculate NDVI from a Landsat image
  • GEE 6: Google Earth Engine Tutorial Pt. VI: Data Management
    • Create and edit fusion tables
    • Upload imagery, vector, and tabular data using Fusion Tables and KMLs
    • Share data with other Google Earth Engine (GEE) users as well as download imagery after manipulation in GEE.
  • GEE 7: Google Earth Engine Tutorial Pt. VII: Charts, Histograms, and Time Series
    • Create a histogram graph from band values of an image collection
    • Create a time series graph from band values of an image collection

Set 2: Prepared by Mr. Ge (Jeff) Pu and Dr. Lindi J. Quackenbush of the State University of New York-College of Environmental Science and Forestry.

This lab focusses on introducing the fundamentals needed to use the GEE API. This lab introduces fundamental terms in GEE and provides guidance through several basic tasks. At the end of this lab you will be able to use GEE to perform the following tasks:

    • Run basic Java commands.
    • Display and clip image and vector.
    • Composite and mosaic images.
    • Explore image collections and their metadata.
    • Filter image collections.
    • Perform simple image band calculations.
    • Explore and construct functions and map these functions over an image collection.
    • Import and export raster and vector.
    • Construct simple graphs based on a set of images e.g. change in vegetation index over time.

This lab introduces several image preprocessing techniques. At the end of this lab you will be able to use GEE to perform the following tasks:

    • Extract image projection information and reproject an image.
    • Register images.
    • Remove shadows.
    • Remove clouds.
    • Compute spectral indices.

This lab focuses on guiding you through several image processing techniques. At the end of this lab you will be able to use GEE to perform the following tasks:

    • Grey-level thresholding
    • Level slicing
    • Image convolution using boxcar kernel
    • Edge detection
    • Pan sharpening
    • Texture analysis

This lab focuses on guiding you through several image processing techniques. At the end of this lab you will be able to use GEE to perform the following tasks:

    • Supervised Classification
    • Classification Accuracy Assessment
    • Unsupervised Classification
    • Spectral Mixture Analysis
    • Image Time Series Analysis