4.36 (90 reviews)
☑ 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
☑ 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
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
- 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!
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: 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
This is very detailed and advanced course covering such important topics as Machine Learning and Change detection in Earth Engine.
This course very informative. would recommend both researchers and professionals to understand Earth Engine. Thank you!
The explanations are very clear. It is explaining all the issues related with the application. Very knowledgeable instructor!
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!
Very robust GEE course for remote sensing applications – the course is very well structured. I had fun doing this course.
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.
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.
Good level of complexity. It includes basic concepts and the several steps and methods needed to work on Machine Learning Analysis, very nice explanation.
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.
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!
yes, it is a good match and delivers what I'm looking for - guided examples and workflows for each type of classification learning.
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
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.
Was a good match, I gained enough knowledge and I'm very glad. Please can the course videos be made available
All the topics were explained very well. Would recommend to anyone who wants to learn about geospatial analysis in Google Earth ENgine.