Spatial Data Management with Google Earth Engine

Section outline

• Select section Introduction

  Introduction

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  Spatial data are key ingredients for spatial analysis. In many GIS projects, you will need to find the spatial data suitable for your project needs, and analyze them in an effective and efficient manner. In this course, you will learn how to retrieve, analyze, and manage spatial data using Google Earth Engine (GEE) and the geemap Python package. GEE is a free cloud computing platform with over 35 petabytes of satellite imagery and geospatial datasets. The geemap Python package enables users to analyze and visualize Earth Engine datasets interactively within a Jupyter-based environment. Upon completion of this course, you will be able to utilize the multi-petabyte Earth Engine Data Catalog for large-scale geospatial analysis. All you need is an Internet browser. 

  This course includes 19 videos with a total length of 9 hours. All Jupyter notebook examples for this course are available at https://tutorials.geemap.org.
  
• Select section Google Earth Engine and geemap workshop at GeoPython Conference 2021

  Google Earth Engine and geemap workshop at GeoPython Conference 2021

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  This is a 1.5-hour geemap workshop I presented at the GeoPython Conference 2021. This workshop gives a brief introduction to Google Earth Engine and some key features of the geemap Python package. The notebook example used in this workshop is available at https://geemap.org/workshops/GeoPython_2021
  

  Activities: 1
• Select section 1. Introduction to Google Earth Engine and geemap

  1. Introduction to Google Earth Engine and geemap

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  This section gives a brief introduction to Google Earth Engine and the geemap Python package. You will also learn how to install geemap using Miniconda and set up a conda environment for interactive mapping with Jupyter notebook.

  Links:

  • Google Earth Engine
  • geemap
  • Miniconda

  Activities: 1
• Select section 2. Earth Engine Feature Collection

  2. Earth Engine Feature Collection

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  In this section, you will learn how to retrieve, analyze, and visualize Earth Engine vector data (i.e., ee.FeatureCollection). 

  Notebook example: 

  • Styling Earth Engine vector data
    

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• Select section 3. Earth Engine Image

  3. Earth Engine Image

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  In this section, you will learn how to retrieve, analyze, and visualize  Earth Engine raster data (i.e., ee.Image).

  Notebook examples:

  • Image overview
    
  • Image visualization
    
  • Image metadata
    
  • Math operations
    
  • Conditional operations
    
  • Image colorbar
    

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• Select section 4. Earth Engine Image Collection

  4. Earth Engine Image Collection

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  In this section, you will learn how to analyze and visualize time-series Earth Engine raster data (i.e., ee.ImageCollection). You will also learn how to create cloud-free Landsat composites with only a few lines of code. 

  Notebook examples:

  • Image collection overview
    
  • Reducing image collection
    
  • Mapping over image collection
    
  • Creating Cloud free composites
  • Visualizing time-series images
    

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• Select section 5. Geospatial Analysis

  5. Geospatial Analysis

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  In this section, you will learn how to compute zonal statistics and extract time-series pixel values from Earth Engine raster data. 

  Notebook examples:

  • Extract time-series pixel values
  • Compute zonal statistics
  • Compute zonal statistics by group

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• Select section 6. Exporting Data

  6. Exporting Data

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  In this section, you will learn how to export Earth Engine datasets and computational results to Google Drive and your local computer. 

  Notebook example: 

  • Exporting Earth Engine data

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• Select section Other resources

  Other resources

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