Satellite Image Processing and Analysis in the Big Data Era
We live in exciting times with a rapidly expanding number of earth observation products being available for better understanding global environmental and socio-economic changes. However, this development poses a number of challenges:
- How to process large remote sensing data sets efficiently?
- Which approaches are suitable for time-series analysis?
- How to extract and analyze patterns and trajectories of land change?
MSc Programme in Geography and Geoinformatics
- 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 convolutional neural networks (CNNs),
- Advanced time-series analysis and machine learning in a cloud environment,
- Land-use modeling,
- Python Application Programming Interface (API); API-based computation with Python for big data processing.
- 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,
- Progression of skills on python-based analysis of satellite data using machine learning,
- Hands-on experience on parameterization, running and evaluation of the performance of convolutional neural networks for pattern recognition tasks using e.g., Google Colab and TensorFlow,
- Advanced use of cloud-based time-series methods, such as LandTrendr/ BFAST using e.g., Googe Earth Engine (GEE), Copernicus Data and Information Access Services (DIAS), System for earth observations, data access, processing & analysis for land monitoring (SEPAL FAO),
- Setting up and evaluation of machine-learning approaches in land-use modeling.
- Integrating knowledge gained from lectures, hands-on exercises and independent readings of up-to-date publications to perform a course project of your choice.
Ability to process and analyze various types and a large amount of remote sensing data sets using advanced cloud-based methods and state-of-the-art machine learning.
The form of teaching is theory exercises combined with lectures and various forms of activation of learning. For the teaching plan, please see Absalon.
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. If you dit not take remote sensing classes, please contact the course responsible prior to enrolling in the class.
- 7,5 ECTS
- Type of assessment
Written assignment, During courseOral examination, 20 minutes
- Type of assessment details
- The written assignment is prepared during the course and must be handed in prior to the exam week. The oral exam uses the written assignment (course project) as its point of departure. There is no preparation for the oral exam. It includes the titles listed in the officially approved reading list. A combined grade is given after the oral exam.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
See learning outcome.
Single subject courses (day)
- Theory exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
The number of seats may be reduced in the late registration period
- Study Board of Geosciences and Management
- Department of Geoscience and Natural Resource Management
- Faculty of Science
- Alexander Prishchepov (4-65707476446d6b72326f7932686f)
Alexander Prishchepov, Rasmus Fensholt
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Courseinformation of students