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?

Enroll in this Remote Sensing for Land Cover Mapping in Google Earth Engine course and master land use land cover classification on the cloud.

In this course we will cover the following topics:

• Unsupervised Classification (Clustering)
• Training Reference data
• Supervised Classification with Landsat
• The Supervised Classification with Sentinel
• Supervised Classification with MODIS
• Change Detection Analysis (Water and Forest Change Analysis)
• Global Land Cover Products (NLCD, Globe Cover, and MODIS Land Cover)

What makes me qualified to teach you?

I am Dr. Alemayehu Midekisa, I have over 10 years of experience in processing and analyzing real big Earth observation data from various sources including Landsat, MODIS, Sentinel-2, SRTM, and other remote sensing products.

I am also the recipient of one of the prestigious NASA Earth and Space Science Fellowships. I teach over 10,000 students on Udemy.

I will provide you with hands-on training with example data, sample scripts, and real-world applications.

By taking this course, you will take your spatial data science skills to the next level by gaining proficiency in processing satellite data, applying classification algorithms,s and assessing classification accuracy using a confusion matrix. We will apply classification using various satellites including Landsat, MODIS, and Sentinel.

Jump in right now to enroll. To get started click the enroll button.