Thomas Goelles

Thomas Goelles

Scientist at Karl-Franzens-Universität Graz and Virtual Vehicle Research

University of Graz

My current research is on avalanches and how to detect them in satellite remote sensing and with lidar data. While, my PhD was in glaciology were I developed numerical models of the Greenland ice sheet and studied black carbon and albedo. Overall I am interested in data analytics and increasing research quality by automating the mundane tasks wherever possible. For this I like to dive deep into interesting technology such as docker, streamlit, dask, FastAPI and of course machine learning.

I co-founded and sold a guiding company in the northernmost town at 78 degrees north. I was also a co-founder at ATSEDA AB - a data science company in Sweden. Currently, I am working on a University spin-off based on our research linked to avalanches and lidar.

Current Positions

 
 
 
 
 
Unverisity of Graz
Scientist
October 2021 – Present Graz, Austria
  • Co-lead for Spin-Off project
  • Avalanche Research
  • Lead Software Architect in 2 Research Projects
  • Development and automatisation
  • Proposal writing
 
 
 
 
 
Senior Scientist
September 2019 – Present Graz, Austria
  • Leading several projects on automotive lidar
  • Data Management
  • Reproducible Science
  • Data Science
  • Lidar testing
  • Proposal writing

Skills

Geophysics
Data Science
Automotive
Python
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Fortran
Mathematica_Logo
Wolfram (Mathematica)

Projects

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Phd_project
During my PhD Project at the University Centre in Svalbard and the Norwegian University of Life Sciences I conducted research on the impact of ice impurities on the Greenland Ice Sheet.
RSnow_AUT
Three year to develop a snow avalanche detection service for the Austrian Alpine region using remote sensing data from Sentinel and automotive lidar data. Each year about 100 people lose their lives in snow avalanches in the European Alps and in addition the annual financial loss due to road closures and damages is estimated to be more than one billion euros in Europe only.
SnowAV_AT
One year exploratory project funded by the Austrian Research Promotion Agency (FFG) to develop a snow avalanche detection service for the Austrian Alpine region using remote sensing data from Sentinel and automotive lidar data.
Sensor FDIR
Data collection and analytics to detect faults close to the lidar unit such as vibration, blocking layers on the cover. Development of testing routines and data analytics tools for the future development of fault detection algorithms.
iLIDS4SAM
iLIDS4SAM is a flagship project for Austria´s future in automated mobility. Automated mobility systems are presently moving towards more complex urban traffic scenarios. The FFG-funded iLIDS4SAM project will enable this transition by developing high performance, low-cost LiDAR sensors with increased field of view and resolution.

Teaching

Current Teaching

  • Spatial Statistical Analysis and Visualisation 2
  • Field trip (snow and avalanches)
  • Modeling in Physical Geography

Past Teaching

  • Spatial Statistical Analysis and Visualisation 1
  • Hydrological Monitoring of Alpine Catchments
  • Practical Course in Hydrology
  • Automotive Sensors and Actuators, Laboratory, TU Graz, 2022/23
  • Geographical field course, 2022/23
  • Geoscientific model development: best practices of software development and an introduction to machine learning, Uni Graz, 2021
  • Field trip (Snow and avalanches), Uni Graz, 2022
  • Snow and ice processes (AGF-212), The University Centre in Svalbard, 2010-2014
  • IPY field school, The University Centre in Svalbard, 2011 & 2012
  • Arctic hydrology and climate (AT-209), The University Centre in Svalbard, 2010
  • 3D-CAD, E&S IT-Consulting, 2006-2007

Recent Publications

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(2023). Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps. EGU General Assembly Conference Abstracts.

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(2023). pointcloudset - A Python package to analyze large datasets of point clouds recorded over time. EGU General Assembly Conference Abstracts.

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(2023). The potential of automotive perception sensors for local snow avalanche monitoring. EGU General Assembly Conference Abstracts.

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(2023). LiMOX – a Physical Lidar Model based on Nvidia Optix Ray Tracing Engine. IEEE Open Journal of Intelligent Transportation Systems.

Cite

(2022). Local snow avalanche monitoring based on automotive lidar and radar sensors,” in International Symposium on Snow 2022.. WSL Institute for Snow and Avalanche Research SLF, 2022.

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Contact

Contact me in German, English, or Swedish