Nora Gourmelon

Nora Gourmelon, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 09.156
Martensstr. 3
91058 Erlangen

  • 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

2022

  • 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.

2019

  • Tapping the potential of Earth Observations

    (FAU Funds)

    Term: April 1, 2019 - March 31, 2022

2022

Journal Articles

Conference Contributions

2021

Journal Articles

Conference Contributions

2020

Conference Contributions

  • 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:
    “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, 22. Oktober
  • 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

  • 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

  • 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”

  • 2022:
    Member of the Steering Committee for the Graduate School Measuring and Modelling Mountain glaciers and ice caps in a Changing Climate (M3OCCA)

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

Praktikum (PR)