Remote Sensing for Land Cover Mapping in Google Earth Engine

Do you want to implement a land cover classification algorithm on the cloud?

Do you want to quickly gain proficiency in digital image processing and classification?

Are you interested in pursuing a career as a geographic data scientist?

Enroll in this Remote Sensing for Land Cover Mapping in Google Earth Engine course to learn how to utilise Google Earth Engine to classify land use and land cover.

The following subjects will be covered in this course:

Classification without supervision (Clustering)
Data for training purposes
Landsat supervised classification
The Sentinel Supervised Classification
MODIS Change Detection Analysis using Supervised Classification (Water and Forest Change Analysis)
Land Cover Products Around the World (NLCD, Globe Cover, and MODIS Land Cover)

What makes me qualified to teach you?

I have over ten years of expertise processing and interpreting large amounts of real-time Earth observation data from a variety of sources, including Landsat, MODIS, Sentinel-2, SRTM, and other remote sensing products.

I’ve also been awarded one of NASA’s renowned Earth and Space Science Fellowships. On Udemy, I have nearly 10,000 pupils.

Hands-on training with example data, sample scripts, and real-world applications will be provided.

This course will help you advance your spatial data science skills by teaching you how to handle satellite data, use classification algorithms, and measure classification accuracy using a confusion matrix. Various satellites, such as Landsat, MODIS, and Sentinel, will be used to classify the data.