• What is GEE and how it works
• Explore GEE API (Javascript and Python)
• DSM workflow (with examples)
  • Compile covariates
  • Extract data from covariates
  • Model training
  • Generate map
  • Export

• Access to over 40 years of Satellite Imagery (with daily updates)
• Algorithms to analyse that data
• Google's computer clusters to analyse the data at scale
• A web-based tool for interactive data exploration

• Access to over 40 years of Satellite Imagery (with daily updates)
• Algorithms to analyse that data
• Google's computer clusters to analyse the data at scale
• A web-based tool for interactive data exploration

> 200 public datasets
> 4000 new images every day
> 5 million images
> 5 petabytes of data

Image and Image Collection

Basic calculations

• At the moment only using Fusion Tables
• Another alternative is using GeoJSON (usually for small datasets)
• Limit of 500,000 rows (also a size limit)

We will be using data SOC from Edgeroi

Let's import data into

Feature Collection

Extract covariates

• Classification and Regression algorithms
• Limited number of algorithms available
  • Tree-like (CART, Random Forest)
  • Perceptron
  • Support Vector Machine
  • Others
• Alternative to use expressions (tree-like structures, linear models)

Train model

This is the step where we can get most speed gain

• The predicion is made at the viewport
  • It depends on the extent
  • It depends on the zoom level (resolution)

It's not always necessary to view the prediction (lazy evaluation)

Apply model

There are multiple alternatives:

It is also possible to export images as Assets

Export