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

4.50 (392 reviews)
Udemy
platform
English
language
Other
category
instructor
3,712
students
6.5 hours
content
Nov 2023
last update
$59.99
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

Description

Land Use/Land Cover Mapping and Change Detection with Machine Learning in Google Earth Engine

Are you ready to elevate your geospatial analysis skills and become proficient in land use and land cover (LULC) mapping and change detection? This comprehensive course is designed to empower users who have a basic background in GIS, geospatial data, and remote sensing with the knowledge and tools required for advanced geospatial analysis.

Course Highlights:

  • Extensive coverage of machine learning algorithms and their practical application

  • In-depth understanding of Google Earth Engine for LULC mapping and change detection

  • Step-by-step guidance on acquiring satellite data, preprocessing, spectral indices calculation, and change map design

  • Real-world projects and practical exercises to reinforce your skills

  • Downloadable materials, including data and Java code files

  • Access to future resources to support your geospatial endeavors

Course Focus:

This course is more than just theory; it's about hands-on learning and practical implementation. You'll gain proficiency in unsupervised and supervised classification strategies for LULC mapping, which is a fundamental skill for GIS and remote sensing analysts. By the end of this course, you'll feel confident in performing advanced geospatial analysis, including machine learning algorithms for mapping and change detection, all using real and openly available data in Google Earth Engine.

Why Choose This Course:

Unlike other training resources, every lecture in this course aims to enhance your GIS and remote sensing skills in a clear and actionable manner. You'll be equipped to analyze spatial data for your own projects and earn recognition from future employers for your advanced GIS skills and knowledge of cutting-edge LULC techniques.

What You'll Learn:

  • Google Earth Engine sign-in and interface navigation

  • Data preprocessing on the cloud and spectral indices calculation

  • Introduction to JavaScript

  • Machine learning theory and its application in GIS

  • Classification of satellite images using various machine learning algorithms (supervised and unsupervised) in Google Earth Engine

  • Training, validation data collection, and accuracy assessment

  • Change detection techniques in Google Earth Engine

  • Completion of your own geospatial project on the cloud

Enroll Today:

If you're a geographer, programmer, social scientist, geologist, or any professional seeking to use LULC maps in your field and want to master state-of-the-art classification algorithms for tasks like land cover and land use mapping, this course is your solution. Sign up now and unlock the confidence and expertise to tackle complex geospatial challenges!

INCLUDED IN THE COURSE: You'll have access to all the data used throughout the course, along with Java code files. Plus, you'll enjoy access to future resources, making this course a valuable investment in your geospatial career. Enroll today and take advantage of these special materials!

Content

Introduction

Introduction

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

Section_Overview
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
BONUS

Screenshots

Google Earth Engine for Machine Learning & Change Detection - Screenshot_01Google Earth Engine for Machine Learning & Change Detection - Screenshot_02Google Earth Engine for Machine Learning & Change Detection - Screenshot_03Google Earth Engine for Machine Learning & Change Detection - Screenshot_04

Reviews

Bruno
August 22, 2023
Very interesting content. The ideas presented are quite promising, but the quality of the presentation is very lacking. The teacher repeats herself very often, specially in very basic information, while at the same time, only briefly glancing over the more complex parts of the content, which makes for a somewhat frustrating experience.
Maciej
July 5, 2023
This was an informative course. I learned some useful methods for GIS classification. However, the content is poorly edited and organised. Videos near the end still introduce the GEE code editor, despite it being used in previous videos. Videos sometimes end abruptly, midway through a sentence. I.e. “Time series trend analysis with Linear Regression” @ 7:12 At times it can be frustrating when the instructor over-explains simple ideas, while functions and parameters more relevant to the course are glanced over or ignored.
Polina
February 23, 2023
This course is amazing that provide better knowledge about image classification in-depth in an easy manner. This course is advisable. Thank You
Chris
February 13, 2023
Instructors need to explain the reasoning, meaning, etc. behind parameters that are included in the code.
Imke
January 18, 2023
I don't wanna be harsh, but I was disappointed by the poor setup of the course. I already took some courses in Udemy and all were very professional, so I expected a certain level of professionality. This one I find is not. While I'm sure the tutor has a lot of knowledge, the learning material and the presentation of her knoweldge does not represent what she is capable of (I'm sure). Aspects I find especially poor: - the audio quality is poor, - the lectures show mutiple times the main aspects of gee, so there is a lot of overlapping content in the tutorials and it feels the tutorials are just multiple standalone lectures put together. This makes the course chaotic. - the explanation of the algorithms, even though informative, are shown in sheets and some read-from-sheet explanation. So you can just google relevant algorithms and you will probably find better explanations on the net - the machine learning part is a only a small part at the end of this course All in all, even though at times informative (especially in the beginning, whe you see the setup and the first coding), the course is chaotic, and due to the poor setup, not very motivating. All in all, this course content could be in length about half of the time, if set up more clear. In conclusion, unfortuately, I would not recommend this course unless you need a certification. There are tutorials on youtube which are setup more clear and professional. Sorry to the tutor, I wish I could give a recommendation.
Iftikhar
November 27, 2022
It was a well-structured and useful course that helps one develop a good understanding of GEE-based Machine Learning and Change Detection
Kabilan
August 30, 2022
Concepts are not clearly explained. English pronuctiation is not proper. Seems like reading a content and more theoratical.
Harold
August 4, 2022
I enjoy listening while watching the video while surfing relevant subject to course content, i.e., I am using Google Earth Pro..
Yuliia
December 15, 2021
The course is well structured. The examples provide a useful guide to further study in Machine Learning for spatial analysis in Earth Engine.
Egor
December 15, 2021
I enjoyed this course, this course is very interesting and informative and also very up to date on Earth Engine unlike other courses on the same topic on Udemy!
Mariya
December 14, 2021
This course is very informative. I world recommend it to both researchers and professionals in GIS and Remonte Sensing.
Olha
October 26, 2021
as usual great content and engaging delivery - I have discivered for me a power of Erath Engine for land use mapping - thanks a lot!
Anja
October 25, 2021
I injoyed this course, it is very useful and interesting if you want to learn to work on the cloud and geospatial analysis
Yuriy
October 1, 2021
this is very professional course. I enjoyed both analysis part of image classification and change detection in Earth Engine and great introduction to JavaScript. I recommend this 5-star course to all.
Ekaterina
September 23, 2021
The instructor has ideally structured this GEE course.My favourite is the time series analysis section,which is quite advanced.

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3294578
udemy ID
7/3/2020
course created date
7/13/2020
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