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Complete Google Earth Engine for Remote Sensing & GIS

Harness the Power of Google Earth Engine for GIS Spatial Data Analysis & Remote Sensing
Instructor:
Minerva Singh
2,829 students enrolled
English [Auto]
Students will gain access to and a thorough knowledge of the Google Earth Engine platform
Carry out pre-processing and processing of satellite data in the cloud
Implement some of the most common GIS techniques on satellite data
Implement time series analysis of multi-temporal optical data
Implement machine learning algorithms on satellite data
Gain proficiency in JavaScript for satellite data processing

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING AND GIS ANALYSIS USING GOOGLE EARTH ENGINE (GEE).

Are you currently enrolled in any of my GIS and remote sensing related courses?

Or perhaps you have prior experience in GIS or tools like R and QGIS?

You don’t want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?

The next step for you is to gain proficiency in satellite remote sensing data analysis and GIS using GEE, a cloud based endeavor by Google that can help process several petra-byte of imagery data

MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING AND GIS DATA ANALYSIS WITH GOOGLE EARTH ENGINE- A planetary-scale platform for Earth science data & analysis; powered by Google’s cloud infrastructure. !

My course provides a foundation to carry out PRACTICAL, real-life remote sensing and GIS analysis tasks in this powerful cloud-supported paltform . By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis.

Why Should You Take My Course?

I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life spatial remote sensing data from different sources and producing publications for international peer reviewed journals.

In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA  will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote  sensing can help us answer.

This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis.

Remote sensing software tools are very expensive and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at a risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using one of the most powerful earth observation data and analysis platform GEE is rapidly demonstrating its importance in the geo-spatial sector and improving your skills in GEE will give you an edge over other job applicants..

This is a fairly comprehensive course, i.e. we will focus on learning the most important and widely encountered remote sensing data processing and and GIS analysis techniques in Google Earth Engine

You will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using within GEE.

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!

ENROLL NOW 🙂

Introduction to Google Earth Engine (GEE)

1
What is Google Earth Engine?
2
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
3
Scripts For the Course

Get Started with GEE

1
Explore the Google Earth Engine (GEE) Interface
2
Sign-up for GEE
3
Explore the Datasets in Google Earth Engine (GEE)
4
GEE Explorer for Satellite Data Analysis
5
Code Editor of GEE

Introduction to the GEE Code Editor

1
Hello to Javascript
2
Read in Display Single-Band Raster Data
3
Read & Visualize Multi-Band Raster Data
4
Start With Image Collections
5
Visualize Vector Data
6
More Feature Data Manipulation
7
Read in Shapefiles
8
Uploading Shapefiles Without Fusion Tables
9
Section 3 Quiz

Common GIS Operations Using Google Earth Engine (GEE)

1
Filter a Feature Collection
2
Create a Buffer Around a Feature Collection
3
Compute Zonal Statistics on Feature Data
4
Filter an Image Collection
5
Filter an Image Collection According to Path and Row
6
Filter and Apply Statistical Function on Each Band
7
Select & Display a Specific Image
8
User Defined ROI
9
Create a Categorical DEM Map
10
Deriving Topographic Products from Elevation Data
11
Section 4 Quiz

More GIS Operations in GEE

1
Clipping a Raster Using a Feature
2
Band Arithmetic on Raster Data in GEE
3
User Defined Functions
4
More Arithmetic Operations in GEE
5
Threshold Operations on Raster Data
6
Threshold With Canny Edge Detector
7
Resampling a Raster
8
Change Raster Resolution
9
Raster to Vector Conversion
10
Vector to Raster Conversion

Plotting and Exporting GEE Data

1
Use of Reducer Function
2
Plot Temporal Variation
3
Spectral Signatures Over Time & Space
4
Grouped Means for Two Raster Bands
5
Apply Simple Linear Regression
6
Export Raster Data
7
Export Data in CSV Format
8
Section 6 Quiz

Working with Optical Data-Landsat

1
Principles Behind Collection of Optical Remote Sensing Data
2
Why Do We Need Pre-Processing of Landsat Data
3
Different Landsat Sensors
4
Pan Sharpening Landsat Images
5
More Pan-Sharpening
6
Create a Landsat Composite
7
Texture Indices-Theory
8
Compute Texture Indices From an Image
9
Spectral Unmixing for Mapping
10
Unsupervised Classification- Theory
11
Unsupervised Classification-K Means Clustering
12
Supervised Classification-Theory

Common Remote Sensing Applications

1
Read in and Visualize Socio-Economic Data
2
Hansen Forest Loss Data
3
Compute Forest Loss at Country Scale with Hansen
4
Compute Forest Loss at Sub-Country Scale with Hansen
5
Section 8 Quiz

Miscellaneous Lectures

1
Quick Primer on Spatial Data
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5 hours on-demand video
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Full lifetime access
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Certificate of Completion