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Available courses

Digital Twin Earth

Course Summary

The Digital Twin Earth course will introduce students to both theoretical foundations and digital ecosystems required for the application of the Digital Twin(s) of the Earth and their added value in the next-generation monitoring and forecasting operations of natural and human activities. The course equips students with both knowledge and hard skills on Digital Twins theories and technological frameworks and will enable them to make use of Digital Twins for a wide range of applications, including but not limited to Climate Change Adaptation. The course is designed with practical examples in the EU countries, Vietnam and beyond.

Aim of the course

To acquire consciousness and technical skills of digital twins and their applications, based on the most recent conceptual frameworks, practical examples and available supporting software solutions.

Teaching methods

The course includes lectures and exercises. 

References

Guo, H., Goodchild, M. F., & Annoni, A. (2020). Manual of Digital Earth (p. 852). Springer Nature.




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Geospatial Web Applications

Course summary and pedagogic objectives

Describe the course and mention some of the learning objectives

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Earth Observation

 

 

 

Course summary

 

This course introduces theories and different techniques that are commonly used in observation of the Earth, using remote sensing data registrations. The course starts with introducing basic knowledge and theories about electromagnetic radiation and atmospheric properties, different sensors (radar + passive) and multi-spectral imageries. 

The second module provides knowledge and skills in digital image processing
techniques including techniques to classify images based on different algorithms. 

The third and last module provides different applications using satellite data e.g. hot-spot mapping, working with vegetation indices, time series of satellite data working with time series of data to map changes, mapping fire events etc.

Aim of the course:
To get knowledge, understanding and practical skills of different remote sensing data and techniques to map and monitor the Earth.

Teaching methods

The course includes lectures and exercises to provide students with both theoretical knowledge and applied skills. There are both theoretical examinations of the different tasks and also includes seminars where you either present your project in real time or upload a video recording. There is no final exam for the course, but you need to complete all assignments of the course.



 

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Geospatial Artificial Intelligence

 

Course content

Students will learn applications of Artificial Intelligence (AI) techniques for spatial modelling and analysis, including predictive modelling. The course has a technical focus, with special emphasize on “evolutionary optimization” and “machine learning (including deep learning)” techniques. Different applications of AI in GIS and RS will be explored in the context of exercises, seminars, and the final project. Ethical aspects of AI will also be touched in a lecture.

 

Teaching methods

The course includes lectures and exercises to provide students with both theoretical knowledge and applied skills. It also includes seminars and self-learning activities, as well as quizzes to assess learning of students. There is no final exam for the course, but a final project where students have to solve a spatial problem using one of the AI techniques that they have studied in the course. All exercises are designed based on Python programming language that makes it a prerequisite for the course.

Literature

Python Machine Learning 3rd ed (2019) 
Evolutionary algorithms (2017)

 

 

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Thesis

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CADEO (4)

GIS

GIS (2)


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