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Student of Natural Sciences, Engineering or Computer Science (f/m/x)
Job Description
Req ID:  5472
Place of work:  Oberpfaffenhofen
Starting date:  schnellstmöglich
Career level:  Student research project and final thesis, Student employment
Type of employment:  Part time, Full-time
Duration of contract:  6 Months

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund)

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 12,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

The Institute of Remote Sensing Methodology is a DLR institute with sites in Oberpfaffenhofen near Munich, Berlin-Adlershof and Neustrelitz in Mecklenburg-Western Pomerania. Together with the German Remote Sensing Data Centre, the Institute forms the Earth Observation Centre (EOC), Germany’s centre of excellence for Earth observation

 

What to expect

Road marking extraction from aerial imagery is a key component in building and maintaining digital and HD maps. Accurate detection and vectorization of markings such as lane lines, arrows, crosswalks, and stop lines can significantly enhance applications in autonomous vehicle localization and navigation, HD map updating, and road condition assessment. Due to challenges in extracting fine details from aerial imagery, using information from other modalities, such as street-view imagery, could be beneficial. Developing robust AI models based on e.g., ViTs, generativeAI, diffusion, … for this task is crucial, as reliable and up-to-date information in large-scale supports both traffic safety and intelligent transportation systems. This thesis will focus on designing and implementing a deep learning-based pipeline to efficiently and effectively extract road markings in vector format, ready for integration into digital mapping frameworks.

 

Your tasks

  • Detecting road markings from Aerial imagery using Deep Neural Networks
  • Exploring Street-View imagery for multimodal Deep Neural Network training

 

Your profile

  • Ongoing Master’s studies in Computer Science, AI, Computer Vision, Geoinformatics, or a related discipline
  • Programming experience in Python
  • Experience in AI and Deep Learning approaches
  • Experience with Pytorch or Tensorflow frameworks
  • Good communication skills and proficiency in English (spoken and written)
  • Basic knowledge of semantic segmentation or/and object detection

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

 

We look forward to getting to know you!

 

If you have any questions about this position (Vacancy-ID 5472) please contact:

 

Dr. Stefan Auer 
Tel.: +49 8153 28 1829 
Apply now