What is Earth Observation?
Earth observation plays an increasingly important role in GIS, remote sensing, GeoAI, and spatial data analysis. Satellites collect enormous amounts of geographic information every day, which can be used to gain insights into changes on Earth. Examples include vegetation analysis, water quality, urban development, agricultural monitoring, and environmental issues.
Using the R programming language, satellite images and remote sensing data can be efficiently processed, analyzed, and automated. This creates a powerful environment for reproducible Earth observation workflows and advanced geographic analyses.
R is used worldwide by researchers, GIS specialists, data analysts, and remote sensing professionals to analyze multispectral and hyperspectral data. Thanks to specialized remote sensing packages, large amounts of satellite data can be efficiently processed and combined with statistical analyses and GeoAI applications.
In this blended learning course, you will work with key remote sensing packages such as RStoolbox, sen2r, satellite, MODIStsp, landsat, hsdar, and hyperspec. You will learn to work with Sentinel-2, Landsat, and other Earth observation sources for classifications, multispectral analyses, and hyperspectral data analysis.
During the blended learning course, you will be introduced to the key capabilities of R for Earth observation and remote sensing. You will learn how to import, process, and analyze satellite images using modern remote sensing packages within R.
Attention will be given to working with Sentinel-2, Landsat, and other satellite sources. You will learn to analyze and apply multispectral and hyperspectral datasets in geographic analyses and classifications.
In addition, you will discover how to automate remote sensing workflows and perform reproducible analyses using scripts in R. You will learn to work with classifications, raster analyses, spectral indices, and image processing for Earth observation projects.
During the blended learning program, you will work with practical datasets and realistic remote sensing applications. Upon completion of the blended learning program, you will be able to independently process, analyze, and visualize satellite data within R.
In addition, you will learn how satellite imagery can be used for spatial analysis, monitoring, and automated workflows. This makes R a powerful tool for professionals who want to apply Earth observation within GIS, environmental, infrastructure, and GeoAI projects.
Do you already have experience with remote sensing or GIS? Then this blended learning course provides an excellent foundation for further exploration of GeoAI, hyperspectral analysis, and advanced Earth observation.
What will you learn in this Blended Learning course?
In this blended learning course, you’ll be introduced to the key capabilities of R for Earth observation and remote sensing. You’ll learn how to import, process, and analyze satellite imagery using modern remote sensing packages within R.
The course covers working with Sentinel-2, Landsat, and other satellite sources. You’ll learn to analyze and apply multispectral and hyperspectral datasets in geographic analyses and classifications.
In addition, you will discover how to automate remote sensing workflows and perform reproducible analyses using scripts in R. You will learn to work with classifications, raster analyses, spectral indices, and image processing for Earth observation projects.
During the blended learning program, you will work with practical datasets and realistic remote sensing applications. Upon completion of the blended learning program, you will be able to independently process, analyze, and visualize satellite data within R.
Do you already have experience with remote sensing or GIS? Then this blended learning program provides an excellent foundation for further specialization in GeoAI, hyperspectral analysis, and advanced Earth observation.
Why choose this Blended Learning R Earth Observation course?
Blended learning combines independent online study with practical, interactive sessions, allowing you to gain theoretical knowledge while also learning to work directly with satellite imagery and remote sensing data in R. In the online modules, you’ll learn how to process, analyze, and automate Earth observation data using modern R packages.
You’ll discover how to work with Sentinel-2, Landsat, and other satellite sources for multispectral and hyperspectral analyses. You’ll also learn to perform classifications, calculate spectral indices, and develop reproducible remote sensing workflows within R.
During the hands-on online sessions, you’ll immediately apply the theory to realistic datasets and Earth observation challenges. You’ll receive guidance from experienced instructors and learn how remote sensing analyses are applied in GIS, environmental research, GeoAI, and spatial monitoring.
The combination of online learning and interactive hands-on experience ensures that you not only understand the basic principles of Earth observation in R, but can also immediately apply this knowledge within professional GIS and remote sensing projects.
