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.
In this section, you will learn how to retrieve, analyze, and visualize Earth Engine vector data (i.e., ee.FeatureCollection).
In this section, you will learn how to retrieve, analyze, and visualize Earth Engine raster data (i.e., ee.Image).
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.
In this section, you will learn how to compute zonal statistics and extract time-series pixel values from Earth Engine raster data.
In this section, you will learn how to export Earth Engine datasets and computational results to Google Drive and your local computer.
A hands-on introduction to applied remote sensing using Google Earth Engine by Ujaval Gandhi.