City:  Kaiserslautern
Date:  Jan 18, 2023

Master Thesis »Obtaining altitude road profiles by using super-resolution imaging«

The Fraunhofer-Gesellschaft ( currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 000 employees work with an annual research budget of 2.9 billion euros. 

In Fraunhofer ITWM, in the division Mathematics for Vehicle Engineering, we work on methods for modelling and analysis of usage variability and reliability in vehicle engineering. A special focus lies on the statistical description of vehicle usage models based on geo-referenced data, planning and evaluating measurement campaigns, as well as the extrapolation of data to design loads. We are developing and implementing new modelling and simulation methods and we are applying these methods in projects with leading industry partners.

High-quality elevation profiles of roads are important for simulating vehicle loads and energy consumption. They are difficult to derive from freely available data sources such as Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs). This is due to the relatively low resolution of 1 arcsecond by 1 arcsecond, which corresponds to about 30 m by 30 m, since the typical road width is much smaller. In addition, roads are man-made constructions that can mark discontinuities in the environment, such as when roads run on bridges.


Currently, we solve these problems using complex interpolation methods and manual processing of special situations. The idea in this project is to develop automatic algorithms that increase the resolution of freely available DEMs by using super-resolution imaging. To this end, we aim to combine high-resolution land use information from freely available road network data, such as the Open Streetmap data, and merge this information with the information from the DEMs. Promising methods here are machine learning techniques, such as deep neural networks, since we can evaluate a large pool of high-resolution (1 m x 1 m) DEMs for specific regions in Germany.


What you will do

  • Develop and test appropriate machine learning algorithms that can combine low and high resolution information;
  • Creating data pools for high quality model training data and additional land use information;
  • Finding appropriate methods for model validation that take into account the special role of roads.


What you bring to the table

  • Bachelor degree in (Geo-)Mathematis or computer science or geography
  • Prior experience in data analytics, AI, statistics, machine learning
  • Ideally prior experience with geospatial data


What you can expect

  • We focus on innovative ideas and design novel solutions. Support us actively in shaping the future!
  • We work in a highly motivated and agile team, together with renowned industry partners.
  • Our institute has been awarded the Fraunhofer Family Logo for its excellent family- and life-phase-oriented working conditions and good framework conditions for flexible working!


We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. 

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!


Questions about this position will be answered by

Dr. Michael Burger


Dr. Jochen Fiedler

Fraunhofer Institute for Industrial Mathematics ITWM 


Requisition Number: 40003                Application Deadline:


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