Google Earth Engine for Machine Learning & Change Detection

Become Expert in Spatial analysis & Remote Sensing for machine learning in land use / land cover in Google Earth Engine

4.36 (90 reviews)



6 hours


Feb 2021

Last Update
Regular Price

What you will learn

Students will gain access to and a thorough knowledge of the Google Earth Engine platform

Implement machine learning algorithms on geospatial (satellite images) data in Earth Engine for LULC mapping

Get introduced and advance JavaScript skills on Google Earth Engine platform

Fully understand the main types of Machine Learning (supervised and unsupervised learning)

Learn how to apply supervised and unsupervised Machine Learning algorithms in Google Earth Engine

Learn how to obtain satellite data, apply image preprocessing, create training and validation data in Google Earth Engine

Implement calculation of change detection (pre and post-event detection) based on spectral indices

You'll have a copy of the codes used in the course for your reference


Land Use/Land Cover mapping and change detection with Machine Learning in Google Earth Engine

This course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks using a variety of different data and applying Machine Learning state of the art algorithms. In addition to improving your skills in JavaScript, this course will make you proficient in Google Earth Engine for land use and land cover (LULC) mapping and change detection. As a result, you will be introduced to the exciting capabilities of Google Earth Engine which is a global leader for cloud computing in Geosciences!

I'm very excited that you found my Google Earth En gine course. This course is designed to equip you with the practical knowledge of unsupervised and supervised classification strategies for Land Use and Land Cover (LULC) mapping, which is one of the core skills for any Geographic Information Systems (GIS) and Remote Sensing analyst. By the end of the course, you will feel confident and completely understand and apply advanced Geospatial analysis including performing Machine Learning algorithms for land use and land cover mapping and spectral indices calculation and change detection. All this you will be able to carry out on the real (and open) data in Google Earth Engine.

This course is different from other training resources. Each lecture seeks to enhance your GIS and Remote Sensing skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing spatial data for your own projects, and gain appreciation from your future employers with your advances GIS skills and knowledge of the cutting edge LULC techniques.

In the course, you will be able to learn how to carry out all stages of LULC mapping from acquiring satellite data to assessing the accuracy of your map and design a beautiful change map readily available to be inserted in your document or report.

The course is ideal for professionals such as geographers, programmers, social scientists, geologists, and all other experts who need to use LULC maps in their field and would like to learn fundamentals of LULC and change detection in GIS. If you're planning to undertake a task that requires to use a state of the art classification algorithms for creating, for instance, land cover and land use maps, this course will give you the confidence you need to understand and solve such geospatial problem.

One important part of the course is the practical exercises. You will be given some precise instructions, codes, and datasets to create LULC maps and change maps using Google Earth Engine.

In this course, I include downloadable practical materials that will teach you:

- How to sign in to Google Earth Engine

- Google Earth Engine interface including its main components and plug-ins

- Learn how to preprocess data on the cloud and calculate spectral indices

- Get introduced to javascript

- Learn the theory behind machine learning and machine learning in GIS

- Learn how to classify satellite images with different machine learning (supervised and unsupervised) algorithms in Google Earth Engine

- Learn how to perform training, validation data collection and accuracy assessment

- Learn how to perform change detection in Google Earth Engine

- Complete Your own geospatial project on the cloud

INCLUDED IN THE COURSE: You will have access to all the data used in the course, along with the Java code files. You will also have access to future resources. Enroll in the course today & take advantage of these special materials!


Google Earth Engine for Machine Learning & Change Detection
Google Earth Engine for Machine Learning & Change Detection
Google Earth Engine for Machine Learning & Change Detection
Google Earth Engine for Machine Learning & Change Detection




Getting started with Google Earth Engine

Why to work with Google Earth Engine?

Lab: Sign up for Google Earth Engine

Interface of Google Earth Engine: Code Editor & Explorer

Overview of datasets in GEE

Basics of Jave Scrips for Google Earth Engine and first steps in image analysis

Lab: Introduction to Javascript

Lab: Declaring variables in Javascript in GEE

Lab: Mapping and Reducing Collection - Landsat Example

Lab: Raster Operations - Calculate NDVI

Lab: Short introduction to functions - Maximum NDVI Example

Lab: Export image data from Google Earth Engine

Theory: on Machine Learning and Image CLassification


Introduction to Machine Learning in GIS and Remote Sensing

Understanding Remote Sensing for LULC mapping

Introduction to LULC classification based on satellite images

Supervised and unsupervised image classification

Stages of LULC supervised classification

Lab: Machine Learning Classification in Google Earth Engine (Explorer)

Unsupervised (K-means) image analysis in Google Earth Engine

Lab: Import images and their visualization in Google Earth Engine

Lab: Image visualisation

Lab: Unsupervised (K-means) image analysis in Google Earth Engine

Supervised image analysis in Google Earth Engine

Common machine Learning algorithms for supervised learning

Lab: Random Forest Classification in Earth Engine

Accuracy Assessment of LULC maps

Lab: Supervised Machine Learning with CART

Lab: Accuracy Assessment in GEE

Introduction to change detection in Google Earth Engine

On change detection: Theory

Mapping Burnt Severity witn Nornalised Burnt Ration (NBR) Index: Theory

Lab: Change Detection in GEE

Your Final Project



Bogdan22 January 2021

This is very detailed and advanced course covering such important topics as Machine Learning and Change detection in Earth Engine.

Ольга4 January 2021

This course very informative. would recommend both researchers and professionals to understand Earth Engine. Thank you!

Олеся16 December 2020

The explanations are very clear. It is explaining all the issues related with the application. Very knowledgeable instructor!

Татьяна16 December 2020

A great and very resourceful course! The course is quite beneficial for my project. The instructor has got the latest information on the subject and has arranged her lectures which are best suited to the student. Thank you!

Елена15 December 2020

Very robust GEE course for remote sensing applications – the course is very well structured. I had fun doing this course.

Ольга15 December 2020

Google earth engine is a useful tool. It is a good course for it – the teacher delivered an excellent introduction to Machine learning and its applications in Earth Engine.

Kjirsten12 December 2020

I expected the course to focus on Machine Learning and Change Detection as the title states, and because this is a topic typically for intermediate or advanced users of GEE I was not expecting full tutorials about how to get started in GEE, etc. I am reviewing the course 80% through and so far we haven't touched on the topic I was expecting to learn about. And the instructor has reviewed or replayed introductory lessons late into the course, possibly by mistake. The instructor is knowledgeable about the topic but unfortunately I haven't learned anything from this course and I cannot recommend it to others seeking to learn about this topic. Fortunately I only paid $10 for it, had I paid the full price I would have been demanding a refund.

Нина19 November 2020

Good level of complexity. It includes basic concepts and the several steps and methods needed to work on Machine Learning Analysis, very nice explanation.

Алия19 November 2020

I enjoyed the way the instructor pushes out the content to the learners. It is interactive and makes learning easy and real fun. I am excited now about Google Earth Engine.

Yuliya19 November 2020

The course is really great. Going Step-by-Step in the coding helps in better understanding the Remote Sensing topic in detail. Overall it was awesome!

Людмила8 October 2020

yes, it is a good match and delivers what I'm looking for - guided examples and workflows for each type of classification learning.

Кирилюк13 September 2020

I injoyed this course, this course is very useful and interesting if you want to learn to work on the cloud and geospatial analysis and perform machine learning-based image classification

Megan25 August 2020

This is a more advanced class in remote sensing. If you have little knowledge of remote sensing/GIS this is not the course for you. I learned some cool features regarding google earth engine API but honestly the help page on the developers.google.com/earth-engine/guides is more thorough. But overall I'm glad for this course so that I didn't have to read through all of the documentation.

Edith15 July 2020

Was a good match, I gained enough knowledge and I'm very glad. Please can the course videos be made available

Jessie13 July 2020

All the topics were explained very well. Would recommend to anyone who wants to learn about geospatial analysis in Google Earth ENgine.


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