Skip to content


Big Data from Space for Planetary Studies 7.5 credits

Stora datamängder från rymden för planetära studier
Second cycle, F7017R
Course syllabus valid: Autumn 2020 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
Second cycle
Grade scale
G U 3 4 5
Space and Atmospeheric Science
Subject group (SCB)
Space Technology
Main field of study
Space Technology

Entry requirements

Basic knowledge in mathematics equivalent to 22.5 credits, for example M0029M - Differential Calculus, M0030M - Linear Algebra and Calculus, M0031M - Linear Algebra and Differential Equations. Basic physics corresponding to 22.5 credits, for example F0004T - Physics 1, F0005T - Physics 2, F0006T Physics3, and knowledge in atmospheric physics corresponding to F7004R - Atmospheric Physics or F7002E - Atmospheric dynamics and Climate. Basic Programming skills (e.g. Introduction to programming for engineers (D0017E), Functions of Several Variables and Computer Tools (M0032M)) Good knowledge in English equivalent to English 6. More information about the English language requirements []

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

After completion of the course, the students should be able to:

  •   Understand and explain sources and application of remotely sensed data for planetary research
  •   Understand and explain the sources and application of long-term climate data for climate change research
  •   Use multi-source remote sensing and ancillary data for planetary monitoring
  •   Understand, deveolp and apply geo-scientific models.
  •   Apply the methods of collecting relevant field data for validation of remotely sensed data and correction of analysis based on these data.  

  • Data types, formats and sources
  • Statistical evaluation of long-term data including measures of central tendency, measures of dispersion, measures of anomaly, distributions, probability analysis, regression analysis, correlations, homogenizations of time series of data, trend analysis, urban effects
  • Spatial data models and their structure, spatial database technology, data supply for geographic information systems
  • Use of data analytic software to create tables and graphs, to convey technical information from big data effectively and accurately
  • Use of equipment and methods to collect field data for validation  

The course consists of classical lectures, combined with homework problems for the students. Solutions of the homework problems are discussed in tutorial classes, peer teaching is used. In parallel the students will work with a project applying data. The project will result in a written and oral presentation. Depending on the circumstances, a different realization of the course can be required. Alternative ways of teaching (e.g., reading course) can be applied, when number of enrolled students is <5.

The course will be examined by a written exam at the end of the course. Additionally, the student’s homework assignments and the written and oral presentation of their course project will be evaluated. The final grade considers all parts of the examination and will be decided when all obligatory elements are fulfilled.
If appropriate, alternative examination methods can be applied.  

Anshuman Bhardwaj

Transition terms
The course F7017R is equal to 7ZZZ

Literature. Valid from Autumn 2020 Sp 1 (May change until 10 weeks before course start)
Bo Wu, Kaichang Di, Jürgen Oberst, Irina Karachevtseva
Planetary Remote Sensing and Mapping
CRC Press
ISBN 9781138584150 - CAT# K376961

Alfred Stein, Freek Van der Meer, Ben Gorte
Spatial Statistics for Remote Sensing

Online ISBN978-0-306-47647-1

Course offered by
Department of Computer Science, Electrical and Space Engineering

CodeDescriptionGrade scaleCrStatusFrom periodTitle
0001Assignment reportU G#1.50MandatoryA20
0002Written examG U 3 4 55.00MandatoryA20
0003Project workU G#1.00MandatoryA20

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by Jonny Johansson, HUL SRT 21 Feb 2020

Last revised
by Jonny Johansson, HUL SRT 21 Feb 2020