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Advanced Geoinformatics

Develop technologies required for analyzing, distributing and visualizing geo-spatial data

Geo-spatial data is the major driver of today’s information society. More Big Geo Data than ever is being created by smart phones, satellites, and sensors. This data is used for an increasing number of scientific purposes that aim to benefit the world. It’s a matter of gathering, analyzing, distributing and visualizing the data to make it fit for specific use, e.g. in systems for improving agricultural practice or creating healthy cities. The technologies supporting these processes are at the core of geoinformatics.

Effectively acquiring and efficiently managing these amounts of data takes more than skills. It also requires keeping pace with ongoing technological developments and understanding how to interpret them. The course Advanced Geoinformatics offers you the possibility to expand your competencies and skills in advanced data acquisition and information extraction methods or focus more on the integration of state-of-the-art methods in geospatial workflows. You will learn to design and develop algorithms, models, and tools that can process geo-spatial data into reliable, actionable information.

For whom is the course relevant?

This course builds on already acquired competences with respect to geospatial data acquisition and geo-information processing and science and thus it is envisaged that you have relevant working experience in the field of geo-information science and earth observation with a focus on the technological side of it, following graduation from a relevant BSc.

course content

For didactic and scheduling reasons, the course requires to choose one of two specializations. A combination of courses from both specialization is NOT possible. The two specializations are earth observation science and geo-information processing, to be chosen upon registration.

Specializations

Choose one upon registration.

Earth Observation Science

Image Analysis

In this course, you will be introduced to advanced image analysis methods enabling to enrich your geoinformation problem solving abilities. Non-linear filters will be introduced for reduction of noise while preserving the boundaries. In addition, interest operators will be introduced to detect stable structures in images that are invariant to scale and rotation transformation. Various methods for dealing with objects in images will be studied. Fuzzy and sub-pixel classification will be introduced to deal with uncertainty and to increase the information content extracted from the imagery. For multisource classification decision trees will be introduced. To automatically detect corresponding image positions, the image matching techniques will be introduced. In particular, area-based matching and feature-based matching will be investigated in this course.

Laser Scanning

Airborne, terrestrial and mobile laser scanning are modern technologies to acquire and monitor the geometry of the Earth's surface and objects above the surface like buildings, trees and road infrastructure. This course provides an overview on the state of the art of these techniques, potential applications, like digital terrain modelling and 3D city modelling, as well as methods to extract geo-information from the recorded point clouds.

Geo-information Processing

Integrated Geospatial Workflows

Thanks to the digital, mobile and IT revolutions, massive amounts of data are nowadays collected at unprecedented spatial, temporal, and thematic scales by both physical and human sensors. For most applications, data availability is less of an issue. What remains an issue is how to convert this data into usable and actionable geo-information that supports decision-making at various scales and that can be further processed to generate knowledge. As a consequence, scientific workflows and scientific workflow management systems become more important for knowledge sharing and ensuring reproducibility.

Selected elements of an integrated workflow are introduced. Methods and techniques that can process massive amounts of spatio-temporal data in (quasi-)real time by using cloud computing technologies will be discussed. This course combines and extents knowledge on semantics, linked data, machine learning and distributed databases, and interactive dashboards presented online.

Spatio-temporal modelling and Analytics

Processes relevant to system Earth, whether natural or man-affected, commonly display variations in space and over time, yet our understanding of their behaviour remains limited. The increase in available monitoring data provides handles for detailed study of these processes. Unravelling the way these processes function and having a mechanism to test hypothesis as well as the possible impacts of interventions is key to contribute to a more sustainable development.  At course end, you will have learnt to make use of the available data in process studies, by a variety of computational techniques.

Learning outcomes

Earth Observation Science

Upon completion of this course, you will be able to:

  • Develop an image processing chain using non-linear filters and mathematical morphology operations for automatic information extraction from images in context of a given problem.
  • Choose and apply a segmentation method to a given image and describe the uncertainty of the obtained result.
  • Make informed decisions on the best classification method for a given set of images and a specific problem.
  • Apply orthorectification to derive orthophoto.
  • Make informed decisions on appropriate image matching method for a given type of data and problem.
  • Evaluate attribute and scale uncertainty and relate it to the quality of derived orthophotos, accuracy of resulting image classification and matching.
  • Explain how point clouds are generated from GNSS, IMU, and range finder measurements and relative sensor registration.
  • Assess the applicability of laser scanning for various tasks, like surface reconstruction and 3D modelling.
  • Design survey plans to acquire point clouds taking into account the accuracy and point density requirements.
  • Evaluate the quality of laser scanning datasets.
  • Determine and apply optimal point cloud processing methods to extract surface descriptions for geometric modelling and point cloud classification.
  • Interpret and analyse point cloud processing results.
Geo-information Processing

Upon completion of this course, you will be able to:

  • Analyse the quality of structured and semi-structured data sources and apply coding solutions for the storage, querying and curation of this data, appropriate for specific application contexts.
  • Apply semantic information integration through knowledge formalisation, semantic enrichment, exploratory querying and data mining.
  • Construct interoperable and reproducible geospatial workflows based on process modelling methods and workflow languages.
  • Make informed decisions on the infrastructural system design for enabling meaningful data integration on the web.
  • Discuss the main spatio-temporal modelling paradigms.
  • Design a conceptual model for a spatiotemporal ABM using UML and the ODD protocol.
  • Implement a basic ABM model, and calibrate this model using behavioural space.
  • Explain to peers the main advantages and limitations of using geo-computational methods.
  • Choose and integrate appropriate geocomputational methods to study a simple spatiotemporal problem.
  • Organize and conduct the modelling and analysis phases required by a simple spatiotemporal project.
  • Apply cloud computing approaches to support and/or realize the main modelling and analysis phases.
  • Evaluate the innovation and societal relevance and impact of the project.

Why choose this course?

If you are currently working with geospatial data and information and have curiosity in recent developments in the acquisition and analysis of high quality primary data or the integration and modelling of geospatial workflows to make business processes more efficient, this course offers an overview of opportunities.

About your certificate

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.

Documentation

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)

550

TOEFL Internet-based Test

79-80

British Council / IELTS

6.0

Cambridge

C2 Proficiency / C1 Advanced

Computer skills

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

Working experience

Your should have  working experience in a geospatial technology domain. In particular, knowledge of and skills in working with GIS and/or digital image processing of remotely sensed data and algorithmic thinking and programming are prerequisites. Fulfillment of these requirements must be shown through course records and/or project/working experience.

Computer skills

The course assumes that participants have programming skills in at least one of the following: R, Python, SQL, C++, Matlab; so that they can easily adapt to different programming environment/scripting languages. 

Key information

Certification
certificate
Duration
10 weeks
Full-time/part-time
Full-time (no part-time programs possible)
Language
100% English taught
Registration deadline
24 February 2020
Starting date
20 April 2020
End date
3 July 2020
Location
Enschede, Netherlands
Accreditation
NVAO
ECTS
14
Tuition fees
2019 / 2020
full-time, institutional
€ 3.125