Machine Learning with Big Earth Data in Google Earth Engine
What you'll learn
- Create training data for supervised classification
- Import Landsat data
- Remove clouds from satellite data
- Run supervised classification algorithm
- Calculate accuracy assessment
- Download land cover classification
- This course has no requirements.
Welcome to the Machine Learning with Big Earth Data in Google Earth Engine course.
This Earth Engine course is without a doubt the most comprehensive course for anyone who wants to apply machine learning using satellite data. Even if you have zero programming experience, this course will take you from beginner to mastery.
The course is taught by an experienced spatial data scientist and former NASA fellow.
The course has been updated to be 2021-ready and you'll be learning the latest tools available on the cloud.
We've taught over 16,000 students how to code and apply spatial data science and cloud computing.
You will have access to example data and sample scripts.
In this course, we will cover the following topics:
Sign Up with Earth Engine
Create training data for supervised classification
Import Landsat data
Remove clouds from satellite data
Run a supervised classification (machine learning) algorithm
Calculate accuracy assessment
Download land cover classification data
The course includes HD video tutorials. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to apply remote sensing and cloud computing for forest monitoring applications.
So, what are you waiting for? Click the buy now button and join the course.
Who this course is for:
- Anyone who wants to learn machine learning using remote sensing data.
I am a geospatial data scientist with 15 plus years of experience. I am a former NASA Earth and Space Science fellow. My research interests include remote sensing, big data and environmental change. More specifically, I am interested in applying big geospatial data, cloud computing and machine learning to solve complex environmental problems, especially land cover change, climate change, water resource, agriculture, and public health.