Complete Google Earth Engine for Remote Sensing & GIS

"Introduction to Google Earth Engine (GEE)
What is Google Earth Engine?
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Scripts For the Course
Get Started with GEE
Explore the Google Earth Engine (GEE) Interface
Sign-up for GEE
Explore the Datasets in Google Earth Engine (GEE)
GEE Explorer for Satellite Data Analysis
Code Editor of GEE
Introduction to the GEE Code Editor
Hello to Javascript
Read in Display Single-Band Raster Data
Read & Visualize Multi-Band Raster Data
Start With Image Collections
Visualize Vector Data
More Feature Data Manipulation
Read in Shapefiles
Uploading Shapefiles Without Fusion Tables
Section 3 Quiz
Common GIS Operations Using Google Earth Engine (GEE)
Filter a Feature Collection
Create a Buffer Around a Feature Collection
Compute Zonal Statistics on Feature Data
Filter an Image Collection
Filter an Image Collection According to Path and Row
Filter and Apply Statistical Function on Each Band
Select & Display a Specific Image
User Defined ROI
Create a Categorical DEM Map
Deriving Topographic Products from Elevation Data
Section 4 Quiz
More GIS Operations in GEE
Clipping a Raster Using a Feature
Band Arithmetic on Raster Data in GEE
User Defined Functions
More Arithmetic Operations in GEE
Threshold Operations on Raster Data
Threshold With Canny Edge Detector
Resampling a Raster
Change Raster Resolution
Raster to Vector Conversion
Vector to Raster Conversion
Plotting and Exporting GEE Data
Use of Reducer Function
Plot Temporal Variation
Spectral Signatures Over Time & Space
Grouped Means for Two Raster Bands
Apply Simple Linear Regression
Export Raster Data
Export Data in CSV Format
Section 6 Quiz
Working with Optical Data-Landsat
Principles Behind Collection of Optical Remote Sensing Data
Why Do We Need Pre-Processing of Landsat Data
Different Landsat Sensors
Pan Sharpening Landsat Images
More Pan-Sharpening
Create a Landsat Composite
Texture Indices-Theory
Compute Texture Indices From an Image
Spectral Unmixing for Mapping
Unsupervised Classification- Theory
Unsupervised Classification-K Means Clustering
Supervised Classification-Theory
Common Remote Sensing Applications
Read in and Visualize Socio-Economic Data
Hansen Forest Loss Data
Compute Forest Loss at Country Scale with Hansen
Compute Forest Loss at Sub-Country Scale with Hansen
Section 8 Quiz
Miscellaneous Lectures
Quick Primer on Spatial Data"