Remote sensing and raster data form the basis of many modern geoinformation applications. In this course, you’ll learn how to process, analyze, and visualize satellite images and other raster datasets using Python. You’ll work with widely used open-source libraries from the GIS and remote sensing fields and apply them directly to hands-on assignments. This will help you develop the skills to independently extract valuable insights from geospatial data.
Raster data and remote sensing form the basis of many applications in GIS, climate research, agriculture, nature conservation, and spatial analysis. Using satellite imagery, aerial photographs, and other raster datasets, large areas can be efficiently analyzed and monitored.
In this blended learning course, you’ll learn how to process, analyze, and visualize raster and remote sensing data using Python. You’ll work with commonly used libraries for managing satellite imagery, performing raster analyses, and processing large geospatial datasets.
The course is suitable for GIS professionals, data analysts, and anyone who wants to learn more about working with raster data and Earth observation. Thanks to the hands-on approach, you’ll not only learn the theory but also apply techniques directly to realistic datasets.
In this blended learning course, you’ll learn the fundamentals of raster analysis and remote sensing with Python. You’ll start by importing, managing, and visualizing raster data and learn how different raster formats are used in geospatial applications.
Among other things, you’ll learn to work with libraries such as Rasterio, Rioxarray, Xarray, and EarthPy for processing raster files. You’ll then work with satellite imagery and remote sensing data and learn how to analyze and prepare this data for further processing.
In addition, you’ll discover how to calculate raster statistics, collect satellite data via STAC services, and work with specialized tools such as Satpy, PyResample, StackSTAC, and Spectral. You’ll also be introduced to image processing techniques using OpenCV and Scikit-image for the classification, detection, and analysis of raster images.
In short: this course is ideal for anyone who wants to use raster data and remote sensing for analysis, monitoring, and gaining valuable insights from satellite and Earth observation data.
Blended learning combines independent online learning with hands-on guidance. You’ll gain access to online course materials that allow you to learn at your own pace how to work with raster data, satellite imagery, and remote sensing techniques. The theory is supported by practical assignments, so you can immediately practice with realistic datasets.
During the guided sessions, you can ask questions, get additional explanations, and work on assignments related to applications in GIS, the environment, climate, and Earth observation. You’ll learn how to perform raster analyses, process satellite images, and generate geospatial insights based on current data.
Upon completion of this course, you will have a solid foundation in raster analysis and remote sensing with Python. You will be able to independently process raster data, analyze satellite images, and perform advanced geospatial analyses for a wide range of applications.
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.
