Satellite Image Processing and Analysis in the Big Data Era
Course 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.
MSc Programme in Geography and Geoinformatics
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.
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 machine learning. If you did not take
remote sensing classes, please contact the course responsible prior
to enrolling in the class.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, During courseOral 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 assessment
See learning outcome.
Single subject courses (day)
- Category
- Hours
- Preparation
- 171
- Theory exercises
- 35
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NIGK22000U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
A
- Capacity
- 22
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student. - Studyboard
- Study Board of Geosciences and Management
Contracting department
- Department of Geoscience and Natural Resource Management
Contracting faculty
- Faculty of Science
Course Coordinator
- Ankit Kariryaa (2-6e784d71763b78823b7178)
Teacher
Ankit Kariryaa, Alexander Prishchepov, Xiaoye Tong, Rasmus Fensholt
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende