Sustainable Gas

funded by the German Ministry for Economics (BMWi)

In 2016, about 50% of the German heat demand was covered by natural gas. Only approx. 13% of the overall heat was provided by renewable energies. This leads to high dependencies on natural gas and uncertainties regarding future security of supply and prices. In order to reduce those issues, numerous technologies aim to produce substitutes for natural gas from renewable energies:

  • Biomethane through the treatment of biogas
  • Substitute Natural Gas (SNG) by methanation of synthesis gas from thermo-chemical conversion of ligneous biomass
  • Hydrogen production by use of renewable electricity (Power-to-Hydrogen, PtH) as well as its conversion to synthetic natural gas (Power-to-Methane, PtM)

The project "SustainableGas" aims to simulate possible market scenarios for the integration of those renewable gases into the German gas market until 2050. A special focus is laid on local environmental and social structures which are integrated additionally to the energy economics into the model in cooperation with project partners from LMU Munich and FAU Erlangen-Nuremberg.

Project Homepage

Forest restoration assessment using Deep Learning for image interpretation

Remote sensing is an important tool for monitoring restoration activities associated with the energy industry. In particular stocking-density surveys are extremely time-intensive activities that can benefit from the development of new technologies. Drones and piloted aircraft acquire imagery that has the potential to assist, but locating and identifying young seedlings in complex scenes is not a trivial task. While a well-trained eye can see them, manual interpretation does not scale operationally over large areas. This poses the main motivation for the project, which is to use state-of-the-art machine learning techniques, in particular, Deep Learning, to effectively develop a tool that will learn from examples how to identify seedlings and therefore be instrumental in the assessment of a forest’s regeneration. In addition to assessing Deep Learning technology for performing automated stocking surveys, this work has the potential to inform about the overall regeneration process, e.g., to investigate whether there are co-location patterns among emerging species (a spatial data mining task) or location patterns associated with animal migration and the like.

This project contributes to the Boreal Ecosystem Recovery and Assessment (BERA) Project.

resaerch team

Michael Fromm (LMU), Matthias Schubert (LMU), Greg McDermid (UoC), Julia Linke (UoC), Guillermo Castilla (Canadian Forest Service, Man Fai Wu(UoC)