PHD Candidate “ Deep Learning-Based Object Outline Extraction from Airborne Sensor Data”

PhD Position
Posted 2 years ago
  • Organization: The Department of Earth Observation Science (EOS) of the Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente
  • Location: Netherlands
  • Deadline: 6 May 2022
  • SALARY INDICATION: € 2,434 – € 3,111

The Department of Earth Observation Science (EOS) of the Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente in Enschede, the Netherlands, has a full-time vacant position for a PhD Candidate.

Your tasks

Maps are quickly becoming outdated,  about 10 % of the objects change annually. This project focuses on methods to automatically interpret newly acquired sensor data to detect and to update the changed objects in the digital topographic map. The sensor data includes high resolution 2D aerial image data and 3D laser scanner data. Our approach is to use the existing (old) topographic maps to learn how various objects appear in these 2D and 3D datasets. The PhD researcher’s task is to design a deep learning approach to generate correct object boundaries in vector format from the airborne sensor data. Closely connected to the task of semantic segmentation of data is the delineation of the data into object boundaries.  How does the boundary of an object appear in the map, and can we transfer this knowledge to draw accurate object polygons from sensor data? What is the generalization ability of the approach for each of the object classes? Can we use prior knowledge on the geometry of the objects (e.g., buildings, roads, trees, water bodies, agricultural fields) to obtain more accurate polygons?

The specific tasks in this project are:

  • Design a method for object boundary generation in vector format from raw or classified sensor data
  • Learn and evaluate the generalization ability for different image resolutions and point densities within a dataset
  • Smart incorporation of expert knowledge for actively learning new situations

Alongside this PhD position, one Postdoc will work on the generation of training data, and one other PhD student will work on the semantic segmentation of the same airborne sensor data.


  • You must hold a MSc degree, obtained no more than 5 years ago, related to computer vision, remote sensing, electrical engineering or geo-informatics, with excellent expertise in acquisition and processing of geo-information, and programming (Python, Tensorflow or PyTorch).
  • You have excellent study results and experience with the use and further development of deep neural networks.
  • Because of the involvement of various Dutch project partners, it is required that you must be at ease communicating and discussing with these partners (in English). In joint meetings research results must be presented to them and the next steps discussed.


We offer you an inspiring multidisciplinary and challenging international and academic environment. The university offers a dynamic ecosystem with enthusiastic colleagues in which internationalisation is an important part of the strategic agenda. You will be employed on a fulltime basis for 4 years, with a qualifier in the first year. Salary and conditions will be in accordance with the Collective Labor Agreement (CAO-NU) of the Dutch Universities.

  • A starting salary of € 2,434.- in the first year and a salary of € 3,111.- in the fourth year gross per month;
  • Tailor-made educational/development programme of at least 6 months (30EC), including visits to conferences;
  • A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%;
  • A solid pension scheme;
  • A total of 41 holiday days in case of full-time employment;
  • Professional and personal development programmes;
  • Costs for moving to Enschede may be reimbursed.


Additional information about this position can be obtained from  Dr. Persello ([email protected]), Dr. Oude Elberink ([email protected]), or from Prof. Vosselman ([email protected]). You are also invited to visit our homepage.

Please submit your application before 7 May 2022 (choose “apply now” below). Your application must include:

  • A cover letter (maximum 1 page A4), emphasizing your specific interest, qualifications and motivations to apply for this position;
  • a full Curriculum Vitae, including a list of all courses attended and grades obtained;
  • Official transcripts from all universities attended
  • Names and e-mail addresses of three references


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Job CategoryPhD/Post-Doc

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