Nora Gourmelon

Nora Gourmelon, M. Sc.

Academic CV

  • Since 11/2020:
    Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg
  • 10/2018 – 11/2020:
    M. Sc. Computer Science, FAU Erlangen-Nürnberg
  • 08/2019 – 12/2019:
    Semester abroad at Norges teknisk-naturvitenskapelige universitet in Trondheim, Norway
  • 10/2014 – 09/2019:
    B. Sc. Computer Science, FAU Erlangen-Nürnberg
  • 09/2016 – 12/2016:
    Semester abroad at Saint Mary`s University in Halifax, Canada



  • International Doctoral Program: Measuring and Modelling Mountain glaciers and ice caps in a Changing Climate (M³OCCA)

    (Third Party Funds Single)

    Term: June 1, 2022 - May 31, 2026
    Funding source: Elitenetzwerk Bayern

    Mountain glaciers and ice caps outside the large ice sheets of Greenland and Antarctica contribute about 41% to the global sea level rise between 1901 to 2018 (IPCC 2021). While the Arctic ice masses are and will remain the main contributors to sea level rise, glacier ice in other mountain regions can be critical for water supply (e.g. irrigation, energy generation, drinking water, but also river transport during dry periods). Furthermore, retreating glaciers also can cause risks and hazards by floods, landslides and rock falls in recently ice-free areas. As a consequence, the Intergovernmental Panel of Climate Change (IPCC) dedicates special attention to the cryosphere (IPCC 2019; IPCC 2021). WMO and UN have defined Essential Climate Variables (ECV) for assessing the status of the cryosphere and its changes. These ECVs should be measured regularly on large scale and are essential to constrain subsequent modelling efforts and predictions.
    The proposed International Doctorate Program (IDP) “Measuring and Modelling Mountain glaciers and ice caps in a Changing ClimAte (M3OCCA)” will substantially contribute to improving our observation and measurement capabilities by creating a unique inter- and transdisciplinary research platform. We will address main uncertainties of current measurements of the cryosphere by developing new instruments and future analysis techniques as well as by considerably advancing geophysical models in glaciology and natural hazard research. The IDP will have a strong component of evolving techniques in the field of deep learning and artificial intelligence (AI) as the data flow from Earth Observation (EO) into modelling increases exponentially. IDP M3OCCA will become the primary focal point for mountain glacier research in Germany and educate emerging
    talents with an interdisciplinary vision as well as excellent technical and soft skills. Within the IDP we combine cutting edge technologies with climate research. We will develop future technologies and transfer knowledge from other disciplines into climate and glacier research to place Bavaria at the forefront in the field of mountain cryosphere research. IDP M3OCCA fully fits into FAU strategic goals and it will leverage on Bavaria’s existing long-term commitment via the super test site Vernagtferner in the Ötztal Alps run by Bavarian Academy of Sciences (BAdW). In addition, we cooperate with the University of Innsbruck and its long-term observatory at Hintereisferner. At those super test sites, we will perform joint measurements, equipment tests, flight campaigns and cross-disciplinary trainings and exercises for our doctoral researchers. We leverage on existing
    instrumentation, measurements and time series. Each of the nine doctoral candidates will be guided by interdisciplinary, international teams comprising university professors, senior scientists and emerging talents from the participating universities and external research organisations.


  • Tapping the potential of Earth Observations

    (FAU Funds)

    Term: April 1, 2019 - March 31, 2022



Journal Articles


Journal Articles

Conference Contributions


Journal Articles

Conference Contributions


Journal Articles

Conference Contributions


Conference Contributions


  • 2024:
    “A Battle of AIs and Whether a Human Would Win,” Pattern Recognition Symposium Winter 2023/24, March 15
  • 2024:
    “AI for Earth,” Invited talk at the Julius-Maximilians-Universität Würzburg, January 22
  • 2023:
    Research presentation for the German Federal Minister of Education and Research Bettina Stark-Watzinger at the Digital Gipfel 2023, November 21
  • 2023:
    Science Pitch at the Digital Gipfel 2023, November 20
  • 2023:
    “AI-based Remote Sensing Applications,” Invited Talk at the Fachsymposium “Artificial Intelligence for Life”, Hochschule für angewandte Wissenschaften Weihenstephan-Triesdorf, October 20
  • 2023:
    “Glacier Monitoring with Computer Vision Models,” Invited talk at the AI, Machine Learning and Data Science Meetup, Voxel51, October 5
  • 2023:
    “CaFFe – a benchmark dataset for glacier calving front extraction from synthetic aperture radar imagery,” Talk (taken over by Julian Klink) at IGARSS 2023, July 19
  • 2023:
    “Conditional Random Fields for improving deep learning-based glacier calving front delineations,” Talk (taken over by Julian Klink) at IGARSS 2023, July 19
  • 2023:
    “Biodiversity and Glacier Monitoring,” Invited Talk at the Green AI Seminar as part of the Public Climate School, FAU, Mai 10
  • 2023:
    “Automatisierte Seevogelerfassung in Luftbild-Videos,” Invited talk at the workshop “KI statt IQ? – Potenziale und Herausforderungen des KI-Einsatzes in der Vogelbeobachtung und im Vogelmonitoring,” German Federal Agency for Nature Conservation, Vilm, January 31
  • 2022:
    “Deep learning-based Calving Front Delineation” and “Bird Monitoring Using Computer Vision Techniques,” Invited Talk at the Green AI Seminar, FAU, November 9
  • 2022:
    “Denoising SAR Imagery for Deep Learning-based Calving Front Segmentation,” Machine Learning for Polar Regions Workshop, Columbia University in the City of New York, June 17
  • 2022:
    “The Birds” and “Calving Fronts and Where to Find Them,” Invited Talk at the Green AI Seminar, FAU, Mai 5
  • 2021:
    “Gletscherfronten und wo sie zu finden sind,” Invited Talk at the Fachsymposium “Artificial Intelligence for Life”, Hochschule für angewandte Wissenschaften Weihenstephan-Triesdorf, October 22
  • 2021:
    “Glacier Fronts and Where to Find Them,” Pattern Recognition Symposium Summer 2021, July 22
  • 2021:
    “Application Area Water (in Different Physical States),” Pattern Recognition Symposium Winter 2020/21, February 18


  • 2023:
    “Calving SAM”, Pattern Recognition Symposium Summer2023, July 11
  • 2023:
    “Conditional Random Fields for Post-processing Deep Learning-based Glacier Calving Front Delineations”, Pattern Recognition Symposium Winter 2023, March 7
  • 2022:
    “Calving Fronts and Where to Find Them: A Multi-Task Model for Automatic Glacier Calving Front Extraction from SAR Imagery”, Cryosphere 2022, Reykjavik, Iceland, August 22 – August 26
  • 2022:
    “Trainable Bilateral Filters for Everybody,” Pattern Recognition Symposium Summer 2022, July 27
  • 2022:
    “The Birds,” Pattern Recognition Symposium Winter 2021/22, March 9
  • 2021:
    “Calving Front Detection in SAR Images using Deep Learning Techniques,” #GeoWoche2021, Arbeitskreis FernerkundungOctober 8, link to abstract, link to poster


  • 2024:
    On t3n’s list of nominees for AI Person of the year
  • 2023:
    German AI-Newcomer Award 2023 in the field natural and life sciences
  • 2023:
    Member of AI Grid
  • 2022:
    Best Poster Award at the Pattern Recognition Symposium Winter 2021/22 for the poster “The Birds”
  • 2021:
    Prize for excellent Master’s thesis “End-use Classification Using High-Resolution Smart Water Meter Data”
  • 2020:
    Best Paper Award at the 5th International Electronic Conference on Water Sciences in the Session “Water Resources Management and the Ecosphere Resilience and Adaptation”

Voluntary Work

  • Since 2023:
    Founding Member of the Mental Health Team at the PRL
  • Since 2023:
    Founding Member of the Equality Team at the PRL
  • Since 2022:
    Member of the Steering Committee for the Graduate School Measuring and Modelling Mountain glaciers and ice caps in a Changing Climate (M3OCCA)
  • Since 2022:
    Presentation of own work (talks, demos, and posters) at the Long Night of Sciences
  • 2019:
    Part of the organizing team of the Local Conference of Youth (LCOY) – Young Climate Conference Germany 2019
  • Journal Reviews:
    The Cryosphere (3), IEEE Transactions on Geoscience and Remote Sensing (3), International Journal of Applied Earth Observation and Geoinformation (2), Ecological Informatics (1), Scientific Reports (1)

Teaching Experience

  • Exercise creation + tutoring:
    Medizintechnik II (SS’21), Introduction to Machine Learning (WS’21/22)
  • Projects:
    Project Remote Sensing (SS’22 – WS’22/23)


No matching records found.


Type Title Status
Project Time Series Calving Front Snakes running
MA thesis Semi-Supervised Learning for Glacier Front Delineation running
MA thesis Calving Fronts and How to Segment Them Using Diffusion Networks running
Project Contrastive Learning for Glacier Segmentation finished
Project Uncertainty Estimation for Transformer-based Glacier Segmentation finished
BA thesis Evaluation of a Pixel-wise Regression Model Solving a Segmentation Task and a Deep Learning Model with the Matthew’s Correlation Coefficient as an Early Stopping Criterion finished
BA thesis Evaluation of a Modified U-Net with Dropout and a Multi-Task Model for Glacier Calving Front Segmentation finished
Project Evaluation of an Attention U-Net for Glacier Segmentation finished
Project Evaluation of an Optimized U-Net for Glacier Segmentation finished
Project Evaluation of a Bayesian U-Net for Glacier Segmentation finished
MA thesis Design and Evaluation of Machine Learning Applications for Space Systems finished
MA thesis Multi-Task Learning for Glacier Segmentation and Calving Front Detection with the nnU-Net Framework finished
Project Temporal Information in Glacier Front Segmentation Using a 3D Conditional Random Field finished
Project Evaluation of Different Loss Functions for Highly Unbalanced Segmentation finished
MA thesis Incorporating Time Series Information into Glacier Segmentation and Front Detection using U-Nets in Combination with LSTMs and Multi-Task Learning finished
MA thesis Image Segmentation via Transformers finished