About us

The Research Group of "Remote Sensing and Spatial Modelling" is part of the Institute of Landscape Ecology (ILOEK) [en] of the University of Münster. We study and teach the acquisition and analysis of spatio-temporal environmental dynamics in a board spectrum of landscape-ecological topics. We combine multi-scale remote sensing data with methods of spatial modelling in order to obtain continuous spatio-temporal information from limited ecological field samples.
The complexity of environmental systems requires the use of modelling strategies that take complex relationships into account. For this reason, we focus on the application of machine learning methods. In addition to their application for research questions in the context of landscape ecology, we also develop new modelling strategies for spatial and spatio-temporal data. Thus, the research group is at the interface between Geoinformatics and Landscape Ecology [en] and has the aim to contribute to an increase in knowledge in ecosystem research via drone data acquisition, satellite data processing, modelling and simulation.

Open position: Doctoral Research Associate; Wissenschaftliche*r Mitarbeiter*in

We are seeking to fill the position of a Doctoral Research Associate commencing on 1 October 2024 until June 2028.

Your tasks:
You will work with us on the project 'Deep Learning in Space and Time,' which focuses on researching both the potential and the challenges of applying deep learning algorithms to spatio-temporal data. Our emphasis will be on graph neural networks applied to Earth observation data, particularly time series of satellite data. You will develop innovative methods to assess the performance of spatio-temporal deep learning models, enhance their transferability across various spatio-temporal contexts, and apply these models to Earth observation data to analyze key parameters related to energy and transport.

The project is part of the CRC/TRR 391: Spatio-temporal Statistics for the Transition of Energy and Transport (TU Dortmund, Ruhr University Bochum). The CRC/TRR 391 is highly interdisciplinary, integrating data-driven and knowledge-driven modelling approaches from statistics, data science, mathematics, computer science, electrical engineering, economics, and transportation/logistics, to create comprehensive solutions for tackling urgent problems arising in the transition to a low-carbon economy and renewable economies.


Our expectations:

• Educational Background: You possess an MSc in Geoinformatics, Data Science, Remote Sensing, Statistics, or a closely related field.
• Experience: You have a background in spatial data analysis and predictive modelling using machine learning techniques. Experience with remote sensing is highly advantageous.
• Technical Skills: You possess strong computing skills, preferably with proficiency in R or Python.
• Innovative Thinking: You have demonstrated scientific creativity and problem-solving abilities.

Advantages for you:

• An excellent research environment and a vibrant interdisciplinary community
• Research that pushes the boundaries of what can be achieved with spatio-temporal data through statistical modelling techniques and artificial intelligence
• Structured supervision and courses that will prepare you for excelling within the respective disciplines, but also for crossing borders between them, including transferable skills and career development. All doctoral researchers will become members of our integrated research training group STAIRS for fostering interdisciplinary exchange and establishing a shared language between disciplines.
• You will be part of the working group for remote sensing & spatial modelling at the University of Münster, a young and dynamic team working at intersection between landscape ecology and geoinformatics.

If you have any questions, please contact Hanna Meyer (hanna.meyer@uni-muenster.de).
Are you interested? Please apply for our project A05 via the collective call of the TRR: https://trr391.tu-dortmund.de/application/

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Winners of poster prizes in the bachelor course "Introduction to Remote Sensing"

As part of the course "Introduction to Remote Sensing" for landscape ecologists and geoinformaticians, the presentation of the course projects took place. We were impressed by the diversity and quality of the projects!

Winners of the poster award this year are:

1st place: Rieke Boelsen & Anna Böttger: Glacier retreat in the Ötztal Alps

2nd place: Franziska Wolf & Maya Aschenbach: Dynamics in the Wadden Sea - development of the outer sand "Norderoogsand"

3rd place: Ferdinand Schicke & Andreas Struffert-Froböse: New Administrative Capital - An analysis of the rapid growth of a new capital for Egypt in the middle of the desert.

4th place: Konstantin Helder & Vincent Flemming: Pine Gulch Fire (Colorado, USA – 2020)

5th place: Nikolas Lefering: The eruption of Tajogaite on La Palma (2021)

6th place: Denise Betha und Nicolas Nierling: Influence of bark beetles on the coniferous stands of the Middle Harz

More News...

Older news can be found in the archive [en] of the Remote Sensing and Spatial Modelling Research Group.