Research assistant: Remote Sensing for Buildings

City growth is something that happens in high-rise as much as horizontally. Measuring the housing stock in block-wise or building-wise detail is one way of mapping out city growth by metrics that go far beyond macro descriptions such as city-wide population growth or changes of administrative borders.

However, in most regions and countries the detail for mapping out city changes in terms of housing stock in 3D is lacking from administrative data. Satellite images and remote-sensing data are an option to fill this void. However, as satellite data provide 2D images only, machines need to be trained to map these images into 3D, possibly through a compilation of google-earth-type street-view data and satellite images. Doing so can provide details on cities that permit studying changes at a level that lie beyond the opportunities of administrative city data.

Job description

The first task is, you will support the project team in data collection, preprocessing and experiments on large-scale building floorspace (footprints + heights) estimation. The second task is, you will assist to build and train a CNN (probably U-net) to predict building footprints and heights in Chinese cities in a large panel.

Your profile

  • Master student from ETH/Uni. Ideally, in the field of computer science, data science or geographical information system.

Skill set:

  • Python, R or similar
  • Experience with CNN (keras, tenserflow or similar)
  • Experience with remote sensing/satellite imagery analysis (google earth engine or similar)
  • Knowledge of GIS is a bonus
  • Knowledge of Amap (Gaode Ditu) is a bonus
  • Experience with computer vision / remote sensing is a plus
  • Languages: English. Knowing Chinese is a bonus
  • You can work 20% during the semester and full time during the semester break.

Your workplace

We offer

  • Standard ETH student RA salary.
  • An interdisciplinary team with good support and a friendly learning environment.
  • A possibility to work onsite/remotely.
  • Potential master thesis/semester project opportunities at MTEC /  INFK, incl. mentoring and networks.

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Curious? So are we.

We look forward to receiving your online application with the following documents:

  • CV
  • Motivation Letter
  • Course transcripts
  • Interview: We might be giving you a small mock task as part of the interview.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the Chair of Applied Economics can be found on our website Questions regarding the position should be directed to Ms. Susie Rao, (no applications).