How do you analyze satellite data using open-source tools? In this one-on-one course, you’ll learn how to process and interpret remote sensing imagery using QGIS, ESA SNAP, and GRASS GIS. You’ll discover how to perform classifications, work with ALOS PALSAR data, and apply techniques such as dimensionality reduction. With personalized guidance and hands-on online modules, you’ll develop immediately applicable skills in geospatial analysis.
Earth observation helps us better understand the world around us. Through satellites, we continuously collect data on the landscape, the climate, and changes on Earth. This satellite data forms the basis for remote sensing and geodata analysis: powerful methods for identifying patterns, monitoring environmental issues, and visually mapping developments.
Thanks to open source tools such as QGIS, ESA SNAP, GRASS GIS, and R, advanced data analysis is more accessible than ever. You can process satellite imagery, apply classifications, and convert complex datasets into clear maps and insights. These techniques can be applied across a wide range of sectors, from urban planning to nature conservation.
What makes Earth observation so valuable is the combination of timeliness, precision, and scale. Whether it involves detecting deforestation, tracking water levels, or analyzing land use: with the right tools and knowledge, you can transform raw data into practical decision-making information.
In this course, you’ll learn how to analyze satellite data and turn it into actionable insights. You’ll discover how to use different types of satellite data, such as optical and radar imagery, to investigate changes in landscape, climate, or urban development.
You’ll work with powerful open-source tools such as QGIS, ESA SNAP, R, and GRASS GIS. With these, you’ll learn to preprocess images, analyze raster data, and recognize patterns. You’ll also work with classification schemes for categorizing land cover and apply dimensionality reduction to make complex data more manageable.
A key component is the Semi-Automatic Classification Plugin (SCP) in QGIS. This allows you to classify and edit remote sensing images in an accessible way. The SCP provides tools to download satellite images, pre-process them, and automatically convert them into visual maps.
Finally, you will learn to work with datasets such as ALOS PALSAR, which allows you to extract detailed information from radar images. You will gain insight into how to apply these techniques in fields such as environmental analysis, spatial planning, or geoinformation projects.
Blended learning combines self-paced online learning with hands-on, interactive modules, allowing you to gain in-depth knowledge of Earth observation and geodata analysis using open-source tools such as QGIS, ESA SNAP, and GRASS GIS. The online lessons give you the freedom to study at your own pace and include clear explanations of remote sensing, satellite image processing, and raster analysis in a GIS environment.
During the hands-on assignments, you’ll work with realistic satellite data and immediately put theory into practice. You’ll learn how to download, preprocess, and analyze satellite data using tools like the Semi-Automatic Classification Plugin (SCP) for QGIS. You’ll also discover how to apply classification schemes, perform dimensionality reduction, and work with radar sources such as ALOS PALSAR.
The combination of flexible online learning and direct application in realistic cases ensures that you not only acquire technical skills but also learn how to effectively apply them to geospatial analysis. Upon completion, you will be able to independently process satellite data, generate insights, and use them for applications such as spatial planning or environmental issues.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
After completing this course, you will be able to:
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.
