Take your geospatial skills to the next level in this hands-on, two-day Google Earth Engine (GEE) advanced course. Built for professionals and researchers with prior GEE experience, this course dives deep into satellite imagery analysis, vegetation indices, and machine learning techniques. Through real-world examples and guided exercises, you’ll gain the confidence and know-how to solve complex environmental challenges using one of the most powerful platforms in geospatial science.
Google Earth Engine Advanced
GoogleGoogle Earth Engine
In today’s data-driven world, geographic information systems (GIS) and platforms like Google Earth Engine are transforming how we understand, manage, and protect our planet. Whether it’s monitoring deforestation, planning urban growth, or tracking agricultural health, analyzing spatial data is essential for making smarter, faster decisions.
At the center of this shift is Google Earth Engine (GEE). It’s a cloud-based platform that gives you access to petabytes of satellite imagery and geospatial datasets — without requiring high-performance hardware. With its browser-based code editor and powerful processing tools, GEE brings large-scale environmental analysis within reach.
What makes this platform so impactful is its ability to link data with specific locations. As a result, it transforms raw numbers into interactive maps and layered visualizations. GIS is already being used across public health, conservation, disaster response, and agriculture. Furthermore, when combined with machine learning, GEE allows for advanced tasks such as land use classification, ecosystem forecasting, and climate impact analysis.
Given these trends, demand is growing for professionals who can work with geospatial data. This course is your opportunity to build those skills and apply them with confidence.
What will you learn
This course builds on your foundational knowledge of GEE and explores advanced techniques in vegetation analysis and machine learning. Through guided exercises and real-world datasets, you’ll gain both theoretical insight and hands-on experience.
To begin with, you’ll work with vegetation indices like NDVI, NDRE, EVI, and MCARI. You’ll apply them to real use cases in agriculture, forestry, and urban planning. In addition, you’ll write and optimize JavaScript code to efficiently process large-scale satellite data.
As the course progresses, you’ll explore supervised and unsupervised machine learning techniques. You’ll use these to classify land cover, detect changes over time, and uncover environmental trends. Moreover, you’ll interpret results using zonal statistics, maps, and graphs. You’ll also evaluate model accuracy using tools such as confusion matrices — ensuring your analysis is both valid and reliable.
Why choose this course
We don’t just teach software—we help you build practical, future-ready skills that you can apply right away. This course is built for learners who want to go beyond the basics and explore the full potential of Google Earth Engine.
Here’s what makes our course stand out:
- Expert instructors with real-world experience in GIS, remote sensing, and GEE
A hands-on approach that emphasizes doing, not just watching - Career-focused learning designed for professionals in environmental monitoring, urban planning, and data science
- Access to real-world case studies that help you apply what you learn in a meaningful way
Course structure
Day 1
On the first day, you’ll dive into vegetation indices and their role in environmental monitoring. You’ll begin by exploring the core concepts behind spectral indices and their practical applications in different fields. Using Sentinel-2 and MODIS satellite datasets, you’ll learn how to calculate common indices like NDVI and EVI, apply them to real-world scenarios, and detect changes over time. You’ll also practice working with time series data and learn to filter and process imagery effectively. To wrap up the day, you’ll create clear visualizations and statistical graphs that help translate raw data into meaningful insights.
Day 2
On the second day, the focus shifts to applying machine learning in a geospatial context. You’ll start by understanding the basics of machine learning and how it integrates with satellite image analysis. Then you’ll build classification models using both supervised and unsupervised methods in GEE. You’ll label training datasets, train your models, and evaluate performance using accuracy metrics like confusion matrices. Throughout the day, real-life case studies will guide your learning, helping you apply these techniques to tasks such as land use classification and environmental change detection.
Learning Outcomes
- The ability to calculate and use vegetation indices for monitoring land use and vegetation health
- Confidence writing efficient, optimized GEE scripts for large-scale data analysis
- A working knowledge of applying machine learning to satellite imagery
- Real-world experience visualizing, interpreting, and validating environmental data
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
Frequently Asked Questions about Google Earth Engine Advanced
In this course, you will learn advanced techniques for processing geospatial data with Google Earth Engine, including vegetation indices, machine learning applications such as supervised classification and Random Forest, environmental monitoring, and developing interactive maps.
Vegetation indices such as NDVI and EVI are used to analyze the health and density of vegetation. In this course, you will learn how to calculate and apply these indices for environmental monitoring and agricultural monitoring using Google Earth Engine.
Yes, this course includes the use of machine learning techniques, such as supervised classification and Random Forest, for analyzing and classifying geospatial data in Google Earth Engine.
You will learn how to use Google Earth Engine for environmental monitoring by applying land cover classification and change detection techniques. This is crucial for projects focused on sustainability and conservation.
The course covers the development of advanced visualization techniques, including interactive map applications and time series, to make complex geospatial data understandable and accessible..
Upon completing the course, you will have advanced skills in Google Earth Engine, enhancing your career opportunities in various fields within the geospatial sector, such as GIS expertise, environmental management, and agricultural monitoring.