Google Earth Engine For Remote Sensing: From Zero To Hero

Get introduced and Become Expert in Geospatial analysis & Remote Sensing for spatial analysis in Google Earth Engine

What you'll learn

Students will gain access to and a thorough knowledge of the Google Earth Engine platform

Get introduced and advance JavaScript skills on Google Earth Engine platform

Learn how to obtain satellite data, apply image preprocessing for Landsat and Sentinel data in in Google Earth Engine

Learn how import and export spatial data (vector and rsater) from / into the platform

Run analyisis for geospatial applications on the cloud

You'll have a copy of the codes used in the course for your reference

Learn how to calculate spectral indices, create maximim composites and work with Big data on cloud

Apply geospatial analysis for real practical example: flood mapping with Sentinel 2 images

Learn image classification (land cover mapping) basics in Earth Engine

Requirements

An interest in working with geospatial data

A working computer with internet connection

Description

Complete Google Earth Engine for Remote Sensing MasterclassThis course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform geospatial analysis tasks with Big Data on the cloud! This course provides you with all the necessary knowledge to start and advance your skills with Geospatial analysis and includes more than 5 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis, and tasks with the Big Data on the cloud.This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). This course emphasizes the importance of understanding the Google Earth Engine platform and JavaScript to be able to implement spatial analysis on the cloud. So, you will learn:a thorough introduction to the Earth Engine Platform, the basics of image analysis (which is essential to understand when you would like to work with Earth Engine)a comprehensive overview of JavaScript basics for spatial analysis. We will cover essential blocks to equip you with the background knowledge and get you started with your analysis on the cloud.You will learn how to import / export data to Earth Engine, how to perform arithmetical image calculationhow to map functions over image collections, and do iterations. We will cover Sentinel and Landsat image pre-processing and analyses for such applications as drought monitoring, flood mapping, and land cover unsupervised and supervised (machine learning algorithms such as Random Forest) classificationWe finish with an introduction to time series trend analysis in GEE.By the end of the course, you will feel confident and completely understand the basics of JavaScript for spatial analysis and you will learn practical geospatial analysis with Big Data on Google Earth Engine cloud. This course will also prepare you for using geospatial analysis with open source and free software tools.One important part of the course is the practical exercises. You will be given some precise instructions, codes, and datasets to create for geospatial analysis in Google Earth Engine.INCLUDED IN THE COURSE: You will have access to all the data used in the course, along with the Java code files. You will also have access to future resources. Enroll in the course today & take advantage of these special materials!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Google Earth Engine

Lecture 2 Why to work with Google Earth Engine?

Lecture 3 Lab: Sign up for Google Earth Engine

Lecture 4 Interface of Google Earth Engine: Code Editor & Explorer

Section 3: Short introduction to spatial and satellite data . theory

Lecture 5 Types of spatial data: vector and raster data

Lecture 6 Introduction to raster data (satellite images)

Lecture 7 Difference between sensors and platforms

Lecture 8 Introduction to Landstat Program of NASA

Lecture 9 Introduction to Sentinel Program of ESA

Lecture 10 Extra lecture: Using cloud platform for spectral indices & land cover analysis

Section 4: Getting started with JavaScrip and geospatial analysis in Google Earth Engine

Lecture 11 Overview of datasets in Earth Engine

Lecture 12 JavaScript - get started!

Lecture 13 Lab: Introduction to JavaScript

Lecture 14 Lab: Mapping and Reducing Collection - Landsat Example

Lecture 15 Lab: Working with image collections and image visualization

Lecture 16 Lab: Image visualisation

Lecture 17 Section 4: Practical Task

Section 5: Image Calculations and Mapping Functions in Earth Engine

Lecture 18 Introduction to image data: Landsat

Lecture 19 Lab: Image Calculations Part 1 - Single Image Calculations

Lecture 20 Lab: Image Calculations Part 2 - Create a composite and calculate NDVI

Lecture 21 Lab: Calculate Zonal Statistics in Earth Engine

Lecture 22 Lab: Short introduction to functions - Maximum NDVI Example

Lecture 23 Lab: How to map a function over an image collection: Example of Landsat and NDVI

Lecture 24 Lab: How to change default names for output image collection

Section 6: Importing / Exporting Data in Google Earth Engine

Lecture 25 Lab: Export image data from Google Earth Engine: an Introduction

Lecture 26 Lab: Importing ratser and vector files into Google Earth Engine

Lecture 27 Lab: Image mosaicking, clipping, reprojection and exporting as tiff to Drive

Lecture 28 Section 6 - Practical Task

Section 7: Examples: Geospatial Analysis in Google Earth Engine

Lecture 29 How to work with spatial data and remote sensing images - theory

Lecture 30 Lab: Iterating function over Image Collection - Example of Drought Monitoring

Lecture 31 Lab: Image preprocessing - Cloud masking of Sentinel 2 images

Lecture 32 Normalized Difference Water Index for flood monitoring - thoery

Lecture 33 Lab: Flood Mapping with Sentinel-2 and NDWI

Lecture 34 Your Project - Flood Mapping

Section 8: Introduction to Land use / land cover (LULC) classification

Lecture 35 Introduction: Machine Learning

Lecture 36 Land use land cover mapping - overview

Lecture 37 Supervised classification with Google Earth Engine (explorer)

Lecture 38 Unsupervised Image Classification and Image Compositing

Lecture 39 Supervised land use mapping with Google Earth Engine and Random Forest

Lecture 40 Task: Image Classification

Lecture 41 Time Series Trend Analysis with Linear Regression: Get Started

Section 9: Outlook

Lecture 42 BONUS

Geographers, Programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field