Remote Sensing for Land Cover Mapping in Google Earth Engine

Remote Sensing for Land Cover Mapping in Google Earth Engine

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)

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. When you are done with this course, you will master methods on how to apply machine learning and supervised classification algorithm using cloud computing and big geospatial data.

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

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

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What you will 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

Rating: 4.4

Level: All Levels

Duration: 2.5 hours

Instructor: Spatial eLearning