Module 5

• Module 5: Supervised Classification

  Supervised classification is arguably the most important classical machine learning techniques in remote sensing. Applications range from generating Land Use/Land Cover maps to change detection. Google Earth Engine is unique suited to do supervised classification at scale. This module covers basic supervised classification workflow and accuracy assessment.
  

  After this module you're able to:

  1. Collect training and test areas
  2. Apply the random forest supervised classification
  3. Assess the accuracy of a classified image

  
  

  The code for these exercises can be found in this repository under Module 5.
  

• Exercise 5.1: Basic Supervised Classification Page
• Submit Assignment 5.1
• Exercise 5.2: Accuracy assessment Page
• Submit Assignment 5.2
• Exercise 5.3: Calculate Zonal Statistics with Images Page
• Submit Assignment 5.3