For a number of years, GAF has been heavily involved in the production of land cover layers for large areas based on high-resolution and very high-resolution satellite data, and the development of processing chains in this context that are as automated as far as possible. Further areas of focus in the development of automated process chains relate to the monitoring of forest areas in context of climate change REDD+ projects and national forest monitoring systems.

This is an opportunity for a Remote Sensing/GIS Expert looking to further develop his/her technically specific career in an international active company.

To strengthen our capacities in the field of (semi-)automatic analysis of remote sensing data and the creation and implementation of modular processing chains, we are now looking for motivated experts with a focus on remote sensing, data analysis, optimising and implementing processing chains, with also a strong forestry and environmental background. GAF AG seeks such experts to support its geo-spatial monitoring projects with a special focus on land use and forest monitoring projects in developing countries.

Your tasks:

  • Implementation of modular process chains for thematic image analysis and for supporting automatic evaluation procedures by close communication with our development teams by provision of assistance for further process enhancements
  • Support of conceptual development of workflows for automatisation of image processing process chains (e.g. time-series analysis, bulk image classification)
  • Implementation of specific evaluation modules/workflows for thematic image analysis with a focus on optical data and/or SAR, mainly in Python (or R)
  • Organising the EO product generation under consideration of budget, schedule and resources
  • Implementation of Quality assurance of output products
  • Support of project reporting and documentation of workflows and tools


What to expect:

  • Interesting and responsible tasks in an exciting and technically cutting-edge environment
  • Mentoring to ensure your solid and rapid integration into our company and in your area of responsibility
  • A young and motivated team
  • Working cross-functional with other teams according to agile principles
  • A friendly and respectful working atmosphere
  • A modern workplace in the conveniently located Munich West area (near Hirschgarten)
  • And much more



Your profile:

  • Educational background: Minimum MSc. in ecology, forestry or similar, preferably with a focus on remote sensing and one of the following disciplines: tropical forestry, land use
  • Profound / deeper understanding of modular process chains for thematic image analysis
  • Minimum 2 years of relevant working experience


Required skills:

  • Advanced knowledge of the mathematical/physical basics of remote sensing and the thematic analysis of satellite data
  • Advanced practical experience in remote sensing/GIS, EO data processing / tropical forest / land use/land cover classification
  • Ability to work in an independent and responsible way
  • Innovative thinking
  • Excellent reporting and communication skills
  • Autonomous and self-responsible working as well as team working skills


Desirable knowledge:

  • Programming experience and skills, especially in Python and/or R
  • Basic knowledge of geodatabases (e.g. PostGIS) and SQL


Language requirements:

  • Excellent command of English and French
  • Demonstrated report writing experience and skills required (English mandatory, French or Spanish as second language would be an asset)



From earliest possible date.
Position for two years with option for indefinite contract.


Please contact:

Olivia Deuter


recruitingatgaf [dot] de


+49 (0) 89 121528-0

Further details:

Would you like to work with us?
If you are interested in any of these career opportunities, please send your full CV in English along with an indication of expected yearly gross salary and your earliest entry date to recruitingatgaf [dot] de.
Please use the keyword “Remote Sensing/GIS Expert for the implementation of modular processing chains”.