Remote Sensing for Land Cover Mapping in Google Earth Engine

Learn machine learning, big data and land use land cover classification using Google Earth Engine cloud API

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

What you’ll learn

  • Learn to apply land use land cover classification using satellite data.
  • Land use land cover change detection analysis.
  • Perform accuracy assessment of land use classifications.
  • Download, and process satellite images.
  • Learn digital image processing.
  • Digitize reference training data.
  • Understand satellite image bands and spectral indices.
  • Predict new land use land cover products.
  • Access global land use land cover products.

Course Content

  • What is Earth Engine? –> 3 lectures • 14min.
  • JavaScript Code Editor –> 2 lectures • 14min.
  • Unsupervised Classification –> 2 lectures • 16min.
  • Training Reference Data –> 1 lecture • 16min.
  • Supervised Classification with Landsat –> 3 lectures • 23min.
  • Supervised Classification with Sentinel –> 3 lectures • 16min.
  • Supervised Classification with MODIS –> 3 lectures • 15min.
  • Change Detection Analysis –> 3 lectures • 19min.
  • Global Land Cover Products –> 3 lectures • 12min.
  • Final Project –> 1 lecture • 1min.
  • Bonus –> 1 lecture • 1min.

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Requirements

  • This course has no requirements..

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

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

Do you want to become a spatial data scientist?

 

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
  • 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 the prestigious NASA Earth and Space Science Fellowship. 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 algorithm and assessing classification accuracy using 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.