See Study finder

Big Geodata Processing

Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at unprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming big geodatasets into actionable information that supports decision-making at various scales, and that can be further processed to generate new knowledge. In this respect, geodata science workflows and modern data management systems are not only key to processing big geospatial datasets but also to sharing the extracted information and knowledge, and to ensuring the reproducibility of the results.

In this course, we present methods and techniques that can be used to build solutions that operate with massive and potentially heterogeneous amounts of spatio-temporal data. Building such a bigdata solution requires: 1/ understanding the advantages and limitations of various types of cloud-based solutions, 2/ using a scalable data management system, 3/ building scalable and robust data mining and machine learning workflows that allow processing large amounts of geodata (e.g., to identify data clusters and create data-driven classification and regression models).


Are you a mid-career professional and national of -and living and working in one of the countries listed here? You might consider applying for a scholarship from the Orange Knowledge Programme (former NFP).

Deadline intake 2019 passed
The application deadline has now passed for intake 2019. We will publish the new call for intake 2020 here. Please have a regular look at our website or follow us on Facebook or LinkedIn for further announcements!


  1. Introduction to big geodata (including then Vs: Volume, Velocity, Variety, Variability, Veracity, Value and Visualization.)
  2. Principles of big geodata management
  3. Principles of big geodata modelling and analysis (clustering, classification and regression tasks).
  4. Setting up a computational solution to store and process big geodata sets
  5. Off-the-shelf vs. do-it-yourself big geodata solutions (e.g. Google Earth Engine vs. HADOOP/SPARK solutions).
  6. Big data solutions to process raster, vector and crowdsourced data
  7. Building scalable workflows
  8. Code versioning
  9. Scientific reproducibility and triangulation

Teaching and learning approach

In this course, the students will learn the fundamentals of big data processing. After that, they will be introduced (via lectures, demos and exercises) to various distributed, cloud, big data solutions.
After that, they will work on a real-life problem involving large datasets. They will work in groups and they will create the necessary workflows to process the data. This requires programming skills and critical thinking to select the “best” algorithm and computational solution.

In this course there will also be a strong emphasis on scientific reproducibility and triangulation. Lectures on archiving data and code will be provided.

About your diploma

Upon successful completion of this course, you will receive a Certificate which will include the name of the course. 

Along with your Certificate you will receive a Course Record providing the name, and if applicable, all the subjects studied as part of the course. It states: the course code, subject, EC credits, exam date, location and the mark awarded.

This certificate course is part of the accredited Master’s  Geo-information Science and earth Observation at ITC. If you decide to take the full Master’s Geo-information Science and Earth Observation at ITC, the Examination Board will give you in principle exemption from the course you followed successfully as a certificate course.

Admission requirements

Academic level and background

Applicants for the Certificate programme should have a Bachelor degree or equivalent from a recognized university in a discipline related to the course, preferably combined with working experience in a relevant field.

Some courses in the Certificate programme or separate modules require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Skills in taught or related subjects are a prerequisite for some courses in the Certificate programme or separate modules. Even if the applicant satisfies the overall admission requirements, acceptance is not automatic.


The faculty accepts transcripts, degrees and diplomas in the following languages: Dutch, English, German, French and Spanish. It is at the discretion of the faculty to require additional English translations of all documents in other languages as well.

English language

As all courses are given in English, proficiency in the English language is a prerequisite.

If you are a national of one of the countries in this list (PDF), you are exempted from an English language test.

Please note: the requirements when applying for fellowships may vary according to the regulations of the fellowship provider.

English language tests: minimum requirements

Only internationally recognised test results are accepted.

TOEFL Paper-based Test (PBT)


TOEFL Internet-based Test


British Council / IELTS



C2 Proficiency / C1 Advanced

Computer skills

Applicants lacking computer experience are strongly advised to follow basic courses in their home countries.

Key information

5 weeks
Full-time (no part-time programs possible)
100% English taught
Enschede, Netherlands
Tuition fees
2019 / 2020
full-time, institutional
€ 1.116