Remote Sensing for Land Cover Mapping in Google Earth Engine

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 in this free udemy course.

What you’ll learn

Remote Sensing for Land Cover Mapping in Google Earth Engine

Learn how to use satellite data to classify land use and land cover.
Analysis of land use and land cover change
Assess the correctness of land use categories.
Satellite images should be downloaded and processed.
Learn how to manipulate digital images.
Data for reference training should be digitised.
Understand the bands and spectral indices in satellite images.
Predict new land cover products for new land uses.
Get global land usage and land cover data.

Requirements

This course has no requirements.

Description

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.

This course is intended for the following individuals:

Anyone who is interested in land use and land cover classification should read this.
Anyone who wants to learn digital image processing and categorization quickly.
Who requires cloud experience to implement land cover classification.
Anyone who want to work as a spatial data scientist is welcome to apply.
Last updated 7/2021

 

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