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Google Earth Engine: Supervised Classification For LULC
Rating: 3.6 out of 5(5 ratings)
18 students
Created byMicah Kutoto
Last updated 9/2025
English

What you'll learn

  • Apply Google Earth Engine for Supervised Classification to generate accurate Land-Use and Land-Cover (LULC) maps using remote sensing datasets.
  • Perform Change Detection analysis in ArcMap (ArcGIS) to identify, quantify, and interpret environmental and land-use changes over time.
  • Evaluate classification accuracy using confusion matrices and validation techniques to ensure reliable and scientifically sound results.
  • Visualize and communicate findings in Excel through advanced data visualization techniques, including charts, graphs, and comparative analysis.

Course content

9 sections • 40 lectures • 4h 2m total length

  • Background and Introduction to Machine Learning.1:34

    This is and Introductory lecture to machine learning and its background.

  • Theory about Land-use Land-cover.2:01

    This is theory about Land Use Land Cover and what are its importance.

  • Theory about Change Detection.1:53

    This lecture is about change detection and is the purpose of doing it in geospatial analysis.

  • Pre-requisite for the Course.3:34

    These are tools you need to have for you to learn this course effectively.

  • Results.3:00

    This lecture will brush you through the results that we shall get at the end of the course.

Requirements

  • Basic knowledge of GIS and Remote Sensing concepts (recommended but not mandatory). Familiarity with Google Earth Engine, ArcMap (ArcGIS), or any GIS software will be an added advantage. A Google Earth Engine account (free to create). Access to ArcMap (ArcGIS Desktop) for Change Detection analysis. Microsoft Excel (or any spreadsheet software) for data visualization. A computer with a stable internet connection to run GEE and download datasets.

Description

Unlock the power of Google Earth Engine (GEE) in this complete guide to Supervised Classification for Land-Use and Land-Cover (LULC) mapping and Change Detection analysis. This hands-on course is designed for students, researchers, GIS professionals, and anyone interested in remote sensing and geospatial analysis.

You will learn how to use Google Earth Engine to perform supervised classification for accurate land-use and land-cover mapping. Step by step, we explore data preprocessing, training sample creation, classification algorithms, and accuracy assessment to ensure reliable results.

Beyond classification, the course covers Change Detection analysis using ArcMap (ArcGIS Desktop), enabling you to identify and quantify environmental changes over time such as urban growth, deforestation, agricultural expansion, and water body variations.

To enhance interpretation and presentation, you will also learn how to use Excel for advanced data visualization, including charts, tables, and comparative statistics to clearly communicate your findings.

By the end of this course, you will have a solid understanding of:

  • Supervised Classification in Google Earth Engine

  • Land-Use Land-Cover mapping techniques

  • Change Detection using ArcMap

  • Accuracy Assessment for LULC results

  • Data visualization and reporting in Excel

This course provides a practical, real-world workflow for performing LULC mapping and change detection projects from start to finish, giving you the skills needed to apply remote sensing and GIS in environmental monitoring, urban planning, agriculture, and natural resource management.

Who this course is for:

  • Intermediate Geospatial Analysts and Geospatial Developers.

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