Land Use Land Cover Classification with Earth Engine API

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Land Use Land Cover Classification with Earth Engine API 4.1 (51 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

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.


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 Land Use Land Cover Classification with Earth Engine API 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)

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 dataapplying 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.

images with 30 meters resolution, have been used to conduct supervised classification for land use and land cover maps.

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