NIGK22000U Satellite Image Processing and Analysis in the Big Data Era

Volume 2025/2026
Education

MSc Programme in Geography and Geoinformatics

Content

In this course, you will learn how to extract relevant variables and analyze patterns from the rapidly expanding data of Earth observation satellites. Alongside geo-coding and reviewing satellite images, we will cover topics related to supervised learning and its various algorithms, including linear regression, random forests, convolutional neural networks, and other methods. We will take a deep dive into evaluating machine learning models’ performance and assessing the accuracy and generalization of results for various remote sensing tasks.

The course welcomes those who would like to advance their knowledge in remote sensing of the environment and socio-economic footprint, pattern recognition, and spatial data analysis or apply the skills they have gained in machine learning to remote sensing of the environment. At the same time, we expect students to have some prior experience in satellite image analysis.

 

Note: This course assumes good programming skills. It is not recommended for those who have not previously taken courses in Python, JavaScript, or other programming languages.
 
Physical & Online: This assumes physical presence, but we support remote participation.

Learning Outcome

Knowledge:

  • Sources of data; currently operating non-commercial and commercial platforms in Earth Observation, with a specific focus on very-high-resolution imagery,
  • Principles of pattern recognition, classification and segmentation using machine learning,
  • Advanced  image processing and classification in a cloud environment,
  • Python Application Programming Interface (API); API-based computation with Python for big data processing.
     

Skills:

  • Hands-on experience in parameterization, running and evaluation of the performance of supervised learning methods using, e.g., Google Colab and PyTorch,
  • Progression of skills in Python-based analysis of satellite data using machine learning,
  • Ability to download, pre-process, and analyze large amounts of satellite imagery with a focus on remote sensing of the geobiosphere and land system science.
  • Advanced use of cloud-based classification via Googe Earth Engine (GEE), Google Colab.

 
Competencies:

  • Ability to process and analyze various types and a large amount of remote sensing data sets using advanced state-of-the-art machine learning and cloud-based methods.

Please see Absalon course page.

BSc in Geography and Geoinformatics or equivalent. Prior experience in satellite remote sensing processing and analysis is expected. Experience in scripting is welcomed.

The course builds on prior knowledge of working with satellite imagery, such as passing via Bachelor level classes such as Introduction to Remote Sensing, Classification of Spatial Data, as well as some Master-level remote sensing classes, such as Remote Sensing of Geobiosphere, Remote Sensing in Land Science Studies, Spatial pattern analysis. However, we will try to accommodate the students without prior experience in remote sensing but with solid experience in programming machine learning. If you did not take remote sensing classes, please contact the course responsible prior to enrolling in the class.
The form of teaching is theory exercises combined with lectures and various forms of activation of learning. For the teaching plan, please see Absalon.
  • Category
  • Hours
  • Preparation
  • 171
  • Theory exercises
  • 35
  • Total
  • 206
Written
Oral
Individual
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Credit
7,5 ECTS
Type of assessment
Written assignment, During course
Oral examination, 20 minutes
Type of assessment details
A set of lab reports developed through the course must be handed in prior to the exam week. The oral exam uses the lab reports as its point of departure. It includes the titles listed in the officially approved reading list. A combined grade is given after the oral exam.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Identical to the ordinary exam.

If the quality of the Lab reports are not acceptable, the student can choose to either hand in a new or revised report.

If the quality of the Lab reports are acceptable, the student can either hand in a revised report or resubmit the original report from the ordinary exam.

The Lab reports must be handed in prior to the re-examination week. The oral exam uses the written assignment as its point of departure. It includes the titles listed in the officially approved reading list. A combined grade is given after the oral exam.

Criteria for exam assesment

See learning outcome.