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FAG3106 Advanced Remote Sensing 7.5 credits

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Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Autumn 2023 Start 30 Oct 2023 programme students

Application code


Headings with content from the Course syllabus FAG3106 (Autumn 2018–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

1)  Earth Observation Big Data

2)  Image Pre-processing

3)  Advanced Image Analysis

4)  Advanced Image Classification

5)  Digital Change Detection

6)  Earth Observation Big Data Analytics

7)  Remote Sensing Applications

Intended learning outcomes

This course intends to provide a comprehensive overview of sophisticated techniques for acquiring remotely sensed data, state-of-the-art algorithms for image processing and analysis, and real-world applications of remote sensing in various fields such as urban planning, environmental monitoring and natural resource management.

Literature and preparations

Specific prerequisites

AG1321 Remote Sensing Technology or equivalent

AG2413 Digital Image Processing and Application or equivalent

Recommended prerequisites

No information inserted


No information inserted


Introductory Digital Image Processing: A Remote Sensing Perspective (4th Edition)

Multitemporal Remote Sensing: Methods and Applications

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • LAB1 - Laboratory exercises, 3.0 credits, grading scale: P, F
  • PRO1 - Project work, 4.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

LAB1 - Laboratory Work, 3.0 credits, grade scale: P, F

PRO1 - Project, 4.5 credits, grade scale: P, F

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Yifang Ban

Supplementary information

The course replaces the previous course F1N5510: Knowledge-based Remote Sensing 7,5 credits.

Postgraduate course

Postgraduate courses at ABE/Geoinformatics