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Advanced Google Earth Engine(GEE) For Spatial Data Analysis

Minerva Singh

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

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

  • You want to quickly analyse large amounts of geospatial data

  • Implement machine learning models on remote sensing data

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

Read more

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

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

  • You want to quickly analyse large amounts of geospatial data

  • Implement machine learning models on remote sensing data

  • 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 endeavour by Google that can help process several petra-byte of imagery data.

 .

My course provides a foundation to carry out  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 have an MPhil (Geography and Environment) from the University of Oxford, UK. 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. You will be introduced to a variety of other datasets as well, including those relating to fires and socio-economic measures.

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 costly, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at risk of prosecution), you will learn to carry out some of the most critical and common remote sensing analysis tasks using one of the most powerful earth observations data and analysis platform.  GEE is rapidly demonstrating its importance in the geospatial 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 essential and widely encountered remote sensing data processing 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

In addition to all the above, you’ll have 

ENROLL NOW :)

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What's inside

Learning objectives

  • Gain robust grounding in the basic and latest features of google earth engine (gee)
  • Learn how to work with in-built shapefiles and imagery data present within gee
  • Learn to upload and analyse your own data in gee
  • Gain proficiency in javascript for operating the gee ecosystem
  • Carry out pre-processing and processing of satellite data in the gee cloud

Syllabus

Welcome to Google Earth Engine (GEE)
Introduction To The Course
Data and Code
What is Google Earth Engine (GEE)?
Read more
Introduction to Basic Spatial Concepts
Get Started with GEE In Your Browser
Explore the Google Earth Engine (GEE) Interface
Sign-up for GEE
Explore the Datasets in Google Earth Engine (GEE)
More GEE Datasets
In Browser Code Editor
Working With the GEE Code Editor
Whistlestop Tour of Javascript
Start Working With JS in the GEE Code Editor
Upload Your Own Data in GEE
Work With Shapefile/Feature Data
Shapefiles/Features in GEE
Filtering Shapefiles
Filter Multiple Countries
Create a Buffer Around a Feature Collection
Compute Zonal Statistics on Feature Data
Intersection Between Feature Collections
Basic Imagery Exploration
Read in Single Band Raster Data
Visualize Imagery Data
Working with DEM Data
Deriving Topographic Porducts
Resampling
Introduction to Image Collections
Read & Visualize Multi-Band Raster Data
Basic Image Filtering-Part 1
Filtering Optical Images to Select Reduced Cloud Covered Images
Image Reducers-Part 1
Of Filtering and Clipping
Define An ROI Interactively
Working With Optical Data in GEE
Different Types of Optical Data in GEE
Principle
Landsat Corrections
Landsat Sensors
Spectral Sharpening
Resolution
Theory Behind Vegetation Index
Compute Common Vegetation Indices (VI)
Use VIs For Masking
Create a Simple Landsat Cloud-Free Composite
Radar Data
Theory Behind Synthetic Aperture Radar (SAR)?
Introduction to Sentinel-1
Read in Sentinel-1 Data
Map Flooding With Sentinel-1
Work With Both VV and VH
ALOS Data
Other Imagery Datasets
Socio-Economic Data Via GEE
Nightlights
Map Fire Patterns With MODIS
Machine Learning in GEE
What Is Machine Learning?
Theoretical Concepts Behind ML
Unsupervised Classification
What is Supervised Learning?
Setting Up a Classification Problem With GEE: Collect Training Data
Define the Supervised Classifier
Classify the Imagery
Some Other Accuracy Metrics
Export Data From GEE
Spectral Signatures
Spectral Unmixing
What is a Tensor?
Upload External Geospatial Data
Object Based Image Analysis (OBIA)
Object Based Image Analysis in GEE
OBIA in GEE-Preliminary Steps
Compute Distance Raster

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Uses Google Earth Engine, which is rapidly demonstrating its importance in the geospatial sector and improving skills in GEE will give learners an edge over other job applicants
Employs actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA to give a practical hands-on experience of working with remote sensing data
Covers essential and widely encountered remote sensing data processing and GIS analysis techniques, making it a comprehensive course for those seeking practical skills
Requires proficiency in JavaScript for operating the GEE ecosystem, which may require learners to acquire or strengthen their JavaScript skills beforehand
Teaches machine learning concepts and implementation within GEE, which is highly relevant for advanced spatial data analysis and gaining insights from remote sensing data
Focuses on a cloud-based platform, which may require learners to have consistent internet access and familiarity with cloud computing environments for optimal learning

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Google Earth Engine(GEE) For Spatial Data Analysis with these activities:
Review Remote Sensing Fundamentals
Reinforce your understanding of remote sensing principles, including electromagnetic spectrum, sensor types, and image resolution, to better grasp the advanced concepts in GEE.
Browse courses on Remote Sensing
Show steps
  • Review textbooks or online resources on remote sensing.
  • Summarize key concepts and definitions.
  • Practice identifying different features in satellite imagery.
Brush Up on JavaScript Basics
Strengthen your JavaScript skills, focusing on syntax, data structures, and control flow, as GEE relies heavily on JavaScript for scripting and analysis.
Browse courses on JavaScript
Show steps
  • Complete an online JavaScript tutorial.
  • Practice writing simple JavaScript functions.
  • Familiarize yourself with JavaScript libraries.
Read 'Google Earth Engine Applications'
Gain a deeper understanding of GEE applications by exploring real-world case studies and examples.
View Putting Knowledge to Work on Amazon
Show steps
  • Read selected chapters focusing on your areas of interest.
  • Summarize the key findings and methodologies used in each case study.
  • Identify potential applications of these techniques to your own projects.
Four other activities
Expand to see all activities and additional details
Show all seven activities
GEE Scripting Challenges
Reinforce your GEE scripting skills by completing coding challenges that involve data filtering, image processing, and analysis.
Show steps
  • Find GEE coding challenges online.
  • Attempt to solve each challenge independently.
  • Compare your solutions with others and learn from their approaches.
Develop a Land Cover Classification Project
Apply your GEE knowledge to create a land cover classification map for a region of interest using satellite imagery and machine learning techniques.
Show steps
  • Select a region of interest and gather relevant satellite imagery.
  • Pre-process the imagery and extract relevant features.
  • Train a machine learning classifier to identify different land cover types.
  • Evaluate the accuracy of your classification and refine your model.
Create a GEE Tutorial
Solidify your understanding of a specific GEE concept by creating a tutorial (written or video) that explains the concept and demonstrates its application.
Show steps
  • Choose a GEE concept you want to explain.
  • Research the concept thoroughly and gather relevant examples.
  • Create a clear and concise tutorial that explains the concept step-by-step.
  • Share your tutorial with others and solicit feedback.
Contribute to a GEE Open Source Project
Deepen your GEE expertise by contributing to an open-source project related to GEE, such as bug fixes, documentation, or new features.
Show steps
  • Find an open-source GEE project on GitHub.
  • Explore the project's codebase and documentation.
  • Identify a bug or feature you can contribute to.
  • Submit a pull request with your changes.

Career center

Learners who complete Advanced Google Earth Engine(GEE) For Spatial Data Analysis will develop knowledge and skills that may be useful to these careers:
Remote Sensing Analyst
A remote sensing analyst uses satellite and aerial imagery to gather data about the Earth's surface, and this course directly prepares learners for that. This role involves processing, analyzing, and interpreting geospatial data to understand environmental changes, manage natural resources, or assist in urban planning. The course's focus on Google Earth Engine, a powerful platform for large-scale geospatial analysis, is particularly relevant. The course will be helpful for processing satellite data from sources like Landsat and JAXA, as well as in using various datasets, including socio-economic and fire data.
See salaries and explore the career path for Remote Sensing Analyst
Geospatial Data Scientist
A geospatial data scientist leverages location data to uncover patterns, trends, and insights for various applications, and a course like this one will help to reach this goal. This role requires proficiency in tools like Google Earth Engine, which is heavily emphasized in this course, to analyze data and build predictive models. The course's focus on satellite remote sensing data and machine learning implementation will be valuable in developing algorithms and models. This course helps build a strong foundation in geospatial analysis techniques, making it a highly relevant first step for aspiring geospatial data scientists.
See salaries and explore the career path for Geospatial Data Scientist
Remote Sensing Technician
A remote sensing technician assists in the collection, processing, and analysis of remote sensing data, and this course can help those interested in this career path. This role often involves working directly with satellite imagery, performing data preprocessing, and generating data products for remote sensing projects. The course's focus on Google Earth Engine, and its hands-on experience with real-world datasets, is a very practical approach to remote sensing data analysis. Someone looking to become a remote sensing technician will be very well prepared if they take this course.
See salaries and explore the career path for Remote Sensing Technician
GIS Analyst
A geographic information systems (GIS) analyst uses GIS software to analyze spatial data, create maps, and perform geospatial analysis, and this course will help develop the skills to be successful in this role. This role often involves working with satellite imagery, processing remote sensing data, and generating actionable information for various stakeholders. The course's focus on Google Earth Engine and its hands-on training using real-world datasets is a great foundation for aspiring GIS analysts. The training in performing analysis on different types of data will be invaluable to your career as a GIS analyst.
See salaries and explore the career path for GIS Analyst
Geographer
A geographer studies the Earth's surface, its inhabitants, and the interactions between them, and a course like this one may prepare someone for a career as a geographer. Geographers use various tools and techniques to analyze spatial data and understand geographic patterns, and this course's focus on Google Earth Engine for geospatial analysis is highly relevant. The course will help in processing satellite data, analyzing socio-economic information, and conducting geographic research. The skills taught in this course can help one succeed in a career as a geographer.
See salaries and explore the career path for Geographer
Climate Change Analyst
A climate change analyst researches the impacts of climate change, and this course may be useful in this endeavor. This role relies heavily on the analysis of large spatial datasets, including remote sensing data, to monitor changes in the environment, including global temperatures, sea levels, and ice cover. The course's focus on Google Earth Engine and its machine learning modules is highly applicable to modeling and analyzing environmental changes. The data processing and analytic skills taught in this course can lead to a successful career as a climate change analyst.
See salaries and explore the career path for Climate Change Analyst
Agricultural Analyst
An agricultural analyst uses data to analyze crop health, predict yields, and optimize agricultural practices, and this course will be useful for this kind of work. This role may involve using remote sensing data and GIS tools, and this course will help to develop these skills. The course's focus on processing satellite imagery and analyzing vegetation indices can be very useful for an agricultural analyst. The skills taught in this course translate directly to the work done by an agricultural analyst daily, including using satellite data to understand conditions.
See salaries and explore the career path for Agricultural Analyst
Conservation Scientist
A conservation scientist studies the environment to develop strategies for preserving, protecting and restoring ecosystems or wildlife populations. This role would benefit from a strong grasp of remote sensing analysis and techniques. The course emphasizes the use of Google Earth Engine for large scale data analysis, which can be used in a conservation setting to monitor changes in habitat, track animal populations, and study the impact of environmental policies. The course will help in gaining proficiency in processing and visualizing data, making it a perfect match for a conservation scientist.
See salaries and explore the career path for Conservation Scientist
Environmental Consultant
Environmental consultants assess environmental issues and provide solutions to clients, and this course may help to prepare for such a role. This role can involve analyzing land use changes, monitoring deforestation, or assessing the impact of human activities, all of which rely heavily on processing geospatial data. The course's emphasis on using satellite data and understanding different types of remote sensing data can be critical for a consultant who uses geospatial analysis. The course also provides hands-on experience to analyze a variety of datasets. This may be useful for environmental assessment and monitoring projects.
See salaries and explore the career path for Environmental Consultant
Disaster Response Coordinator
A disaster response coordinator helps plan, manage, and coordinate disaster response efforts, and this course may be useful to someone in this career path. This role requires the ability to quickly analyze geospatial data such as satellite images and create situation maps to understand the impacted areas. The skills taught in this course, including remote sensing data analysis and data visualization, are directly applicable to the work of a disaster response coordinator. The use of Google Earth Engine allows for quick processing of large datasets, which is essential in an emergency response setting.
See salaries and explore the career path for Disaster Response Coordinator
Urban Planner
An urban planner helps create sustainable and functional cities, and these decisions could be informed by the course. This role involves analyzing spatial data to understand urban growth patterns, plan infrastructure development, and assess the impact of urban expansion. The training in Google Earth Engine and the ability to process large datasets could help in evaluating the impact of policies, identifying optimal locations for new projects, tracking urban sprawl, monitoring green spaces, and evaluating socio-economic factors, making this training very useful for someone wanting to enter urban planning.
See salaries and explore the career path for Urban Planner
Natural Resource Manager
A natural resource manager oversees the use and management of natural resources, and this course may help one prepare for this kind of work. This role involves using spatial data for monitoring forest cover, tracking biodiversity, assessing water resources, and understanding land use changes. The course's focus on remote sensing data, including satellite and radar data, is highly relevant for those seeking a natural resource management role. The training in analyzing various datasets, including fire and socio-economic data, would be useful when managing natural resources.
See salaries and explore the career path for Natural Resource Manager
Cartographer
A cartographer designs and creates maps, and the skills developed in this course may be useful in this kind of work. This role involves understanding spatial relationships, data visualization, and map production, which can be assisted by the practical nature of this course. While traditional cartography involves manual drafting, modern cartographers use GIS software and remote sensing data, and this course will help with this process. The course's focus on processing and analyzing spatial data in Google Earth Engine gives great skills to aspiring cartographers.
See salaries and explore the career path for Cartographer
Data Visualization Specialist
A data visualization specialist is responsible for transforming complex data into easily understandable visuals, and this course may be useful to someone in such a career. While not solely focused on visualization, the course demonstrates how to visualize spatial data, and introduces Javascript which is important for this career. Remote sensing data is especially large and complex, and an expert in visualization could use the skills developed in this course to interpret and display data, making it easier to understand to a client. This course may help someone grow in their career as a data visualization specialist.
See salaries and explore the career path for Data Visualization Specialist
Machine Learning Engineer
A machine learning engineer builds and implements machine learning models, and this course may be useful to someone in this role. This role involves developing algorithms and models to analyze data, and the course's emphasis on machine learning within the Google Earth Engine platform could help with this process. The course also provides practical experience in implementing machine learning models on remote sensing data. This course helps build a foundation for anyone wishing to grow in their career as a machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Advanced Google Earth Engine(GEE) For Spatial Data Analysis.
Cover image
Putting Knowledge to Work
Save
Provides a comprehensive overview of Google Earth Engine and its applications in various fields such as agriculture, forestry, and urban planning. It serves as a valuable reference for understanding real-world use cases and implementing advanced spatial data analysis techniques. The book offers practical examples and case studies that complement the course material, enhancing your ability to apply GEE to solve complex problems. It is commonly used by researchers and practitioners in the geospatial field.
Putting Knowledge to Work
Paperback
Check
Putting Knowledge to Work
Kindle Edition
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Effort
6.5 total hours
Via
Udemy
Instructor
Minerva Singh
Language
English
Good to know
Uses Google Earth Engine, which is rapidly demonstrating its importance in the geospatial sector and improving skills in GEE will give learners an edge over other job applicants
Employs actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA to give a practical hands-on experience of working with remote sensing data
Covers essential and widely encountered remote sensing data processing and GIS analysis techniques, making it a comprehensive course for those seeking practical skills
Requires proficiency in JavaScript for operating the GEE ecosystem, which may require learners to acquire or strengthen their JavaScript skills beforehand
Teaches machine learning concepts and implementation within GEE, which is highly relevant for advanced spatial data analysis and gaining insights from remote sensing data
Focuses on a cloud-based platform, which may require learners to have consistent internet access and familiarity with cloud computing environments for optimal learning
Share and help others discover this course.
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