Advanced Google Earth Engine(GEE) For Spatial Data Analysis

Sign up for Google Earth Engine with a Gmail account to access the Earth Engine Code Editor and API, then learn to write code to access and visualize geospatial data.

Explore the vast catalog of more than 700 Google Earth Engine datasets, from Sentinel and climate data to high‑resolution imagery, terrain, nightlights, and human modification layers.

This whistle-stop tour introduces basic JavaScript concepts essential for using Google Earth Engine’s code editor, covering variables, strings, numbers, lists, dictionaries, comments, and inbuilt functions.

learn how to upload your own data to Google Earth Engine by ingesting shapefiles or individual files as assets, monitor ingest progress, handle errors, and understand projection caveats.

Learn how shapefiles and feature collections organize points, polygons, and lines in Google Earth Engine, including importing external data and manually defining custom geometries.

Filter shapefiles in Google Earth Engine by country name or code to zoom into a specific country, using a world feature collection and selecting Thailand and Cambodia.

Explore intersecting overlapping feature collections in Google Earth Engine by filtering the ecoregions global dataset to Thailand, defining a custom intersect function, and applying symbology.

Filter optical Landsat or Sentinel data to select images with minimal cloud cover using a cloud cover threshold and surface reflectance, applying filter bounds and a median reducer for clarity.

Learn to apply image reducers on image collections in Google Earth Engine, filter to Cambodia 2018, and reduce over space and time with mean, median, minimum, maximum.

Explore three main optical data types in Google Earth Engine—MODIS, Landsat, and Sentinel—and their data catalogs, resolutions, and products for vegetation indices, fire, land cover, and change detection.

Explore computing common vegetation indices with Google Earth Engine using Sentinel surface reflectance data, applying normalized difference formulas across bands b3, b2, b12, filtering by Bolivia, and visualizing results.

Learn to derive vegetation indices from optical data and create masks for water and burn scars using thresholding, clipping to Bolivia, and visualization in Google Earth Engine.

Explore how Google Earth Engine aggregates diverse datasets—Landsat, Sentinel, night lights, forest change, population, and malaria data—and map accessibility to cities using the 2015 Global Accessibility to Cities dataset.

Understand supervised classification theory and its use of training sites and spectral signatures to assign pixels, and review common algorithms like minimum distance, maximum likelihood, spectral angle mapper, and EM.

Prepare Landsat-based training data, create a 75/25 training-testing split, sample seven bands from the clipped amazonian Bolivia scene, and train a random forest classifier with 100 trees.

Learn how to export imagery, shapefiles, and feature collections from Google Earth Engine to Google Drive, manage export tasks, set scale and max pixels, and download training samples.

Learn to upload external geospatial data to Google Earth Engine, including shapefiles, manage assets and ingestion status, and share datasets for use in JavaScript or Python via Colab.

Explore how distributed computing frameworks coordinate parallel tasks, ensure fault tolerance, optimize resource allocation, and scale with growth through real-world examples like Hadoop and Spark.