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  • FAO CB4WA: Google Earth Engine (EN)
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  • Course data
    General
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    Questions and Answers Forum
    Introduction
    Module 1
    Exercise 1.1: Introduction to JavaScript for Google Earth Engine
    Module 2
    Exercise 2.1
    Submit Assignment 2.1
    Exercise 2.2
    Submit Assignment 2.2
    Exercise 2.3
    Submit Assignment 2.3
    Module 3
    Exercise 3.1: Filter
    Submit Assignment 3.1
    Exercise 3.2: Creating Mosaics and Composites from ImageCollections
    Submit Assignment 3.2
    Exercise 3.3: Map
    Data for Exercise 3.3
    Submit Assignment 3.3
    Exercise 3.4: Reduce
    Submit Assignment 3.4
    Module 4
    Exercise 4.1: Calcuating indicies
    Submit Assignment 4.1
    Module 5
    Exercise 5.1: Basic Supervised Classification
    Submit Assignment 5.1
    Exercise 5.2: Accuracy assessment
    Submit Assignment 5.2
    Exercise 5.3: Calculate Zonal Statistics with Images
    Submit Assignment 5.3
    Module 6
    Exercise 6.1: How to deploy a web application from GEE script
    Exercise 6.2: Deploy your first web application
    Tutorial 6.2
    Submit Assignment 6.2
    Other resources
    End-to-End Google Earth Engine
    Spatial Data Management with Google Earth Engine
    YouTube Channel Qiusheng Wu
    Course evaluation & certificate
    Course evaluation
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    Course Content
    1. General
    2. Introduction
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    5. Module 2
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    8. Module 5
    9. Module 6
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    FAO CB4WA: Google Earth Engine (EN)

    1. Home
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    3. CB4WA GEE EN
    4. Module 5
    • General

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    • Introduction
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    • Module 1
    • Module 2
    • Module 3
    • Module 4
    • Module 5
    • Module 6
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    • 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
    ◄Module 4Module 6►

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