For example, using Google Earth Engine and R, organizations can analyze satellite imagery to assess vegetation, drought, deforestation, water management, and climate change on a global scale. In this blended learning course, you’ll learn how to integrate Google Earth Engine with R for cloud-based remote sensing and large-scale spatial analysis.
This blended learning course focuses on performing large-scale spatial analyses and remote sensing workflows with Google Earth Engine using the R programming language. Within Geo-ICT, this is important for issues related to satellite data, climate change, vegetation, water management, drought, land use, and monitoring of large areas.
Satellite imagery and geographic datasets are processed in the cloud without requiring all data to be downloaded locally first. Examples include analyzing Sentinel and Landsat imagery, calculating vegetation indices, performing time-series analyses, and monitoring changes in landscape, water, or built-up areas. This creates a powerful environment for remote sensing, GIS, and Geo-ICT workflows.
What makes R powerful is the combination of programmability, statistics, and reproducible analysis workflows. When combined with GEE, this is augmented by cloud computing and access to large satellite data sources. This allows analyses to be scaled up to larger areas and longer time series.
In this blended learning course, you will work with packages such as rgee and mapview. You will learn to control Google Earth Engine from within R, select satellite data, perform analyses, and interactively visualize results.
In addition, R offers extensive capabilities for combining Earth Engine results with statistics, visualization, reporting, and other Geo-ICT workflows. This makes this blended learning course particularly relevant for GIS specialists, remote sensing specialists, data analysts, researchers, and Geo-ICT professionals who wish to perform cloud-based satellite analyses.
In this blended learning course, you will be introduced to the key capabilities of Google Earth Engine within R. You will learn how to connect to Earth Engine, select datasets, and process satellite imagery within reproducible R workflows. You will work with packages such as rgee and mapview.
The course covers remote sensing, satellite data sources, cloud processing, time series, and interactive visualizations. You will learn how to calculate vegetation indices, analyze changes, and translate results into actionable geographic insights.
You’ll also learn how to use R to automate GEE analyses and combine them with other data sources. Think of applications for drought monitoring, land use, water management, climate adaptation, nature conservation, and spatial policy analysis.
During the blended learning program, you will work with practical datasets and learn how to set up reproducible Google Earth Engine workflows in R. Upon completion, you will be able to independently analyze satellite data and create interactive results for GIS, remote sensing, and Geo-ICT projects.
Do you already have experience with R Spatial Basics, R Hydrology, or R Visualization? Then this blended learning course is a logical next step toward cloud-based remote sensing and large-scale spatial analysis.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the fundamentals of GEE and its practical application in R. In the online modules, you’ll learn how to select, process, and analyze satellite data using cloud-based tools.
You’ll discover how to work with Earth Engine datasets, time series, vegetation indices, and interactive maps. You’ll also learn how to set up reproducible analyses so that results are transparent and repeatable across different areas and projects. Thanks to unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll apply the theory directly to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute Google Earth Engine workflows using packages such as rgee and mapview.
The combination of online learning and interactive hands-on experience ensures that you not only learn how satellite data is technically processed, but also how to translate this data into actionable insights. After completing the blended learning program, you will be able to professionally apply R and Google Earth Engine for remote sensing, climate data, monitoring, and large-scale spatial analyses.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of R and GIS is helpful, but experience with Google Earth Engine is not required. During the blended learning course, Earth Engine workflows, satellite data, and cloud processing will be explained step by step using practical Geo-ICT examples.
During the blended learning program, you will work with satellite data such as Sentinel and Landsat within Google Earth Engine. You will use this data to analyze vegetation, water, land use, drought, and spatial changes.
R and Google Earth Engine are used for remote sensing, climate monitoring, vegetation analysis, water management, drought analysis, land-use classification, monitoring of land-use changes, and large-scale geographic analyses.
Google Earth Engine makes it possible to process large amounts of satellite data in the cloud without downloading everything locally. When used with R, analyses can be automated in a reproducible manner and integrated with statistical, visualization, and geospatial workflows.
During the blended learning program, you will work with tools such as rgee and mapview to integrate with Google Earth Engine, create interactive visualizations, and develop satellite data and remote sensing workflows.
