Latest Posts

15/05/2023
Outreach

AI Newcomer Award 2023 goes to Nora Gourmelon from FAU’s Pattern Recognition Lab

Nora Gourmelon (Photo: second from right) is honored with the AI Newcomer Award 2023 in the field of natural and life sciences for her research in Green AI, a research field that tackles sustainability-related problems with AI.

In her current work, conducted as part of the International Doctoral Program (IDP) “Measuring and Modeling Mountain glaciers and ice caps in a Changing ClimAte (M³OCCA),” she is developing deep-learning techniques for extracting glacier front positions from satellite imagery.

When asked what the award means to her, Gourmelon responds: “The award helps to raise awareness of how you can also get involved in biodiversity and climate protection as a computer scientist. In addition, I am, of course, also very pleased about the great recognition for my research to date.”

The AI Newcomer Award is granted by the German Association of Computer Science (Gesellschaft für Informatik) to young researchers under 30 years for innovative developments in the area of artificial intelligence.

The award ceremony took place in Berlin on April 26 as part of “KI-Camp 2023,” an event for young AI researchers organized by the German Association of Computer Science and the German Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung).

The recording of the ceremony will be published here soon.

The award has also attracted the attention of the media and the press!

02/05/2023
Event

Invited talk on „Keeping track of change – Monitoring Antarctic calving front dynamics with earth observation and deep learning“

The Institute of Geography at FAU Erlangen-Nürnberg will host an invited talk by Dr. Celia Baumhoer (DLR/DFG Oberpfaffenhofen).

When: Wednesday, 14.06.2023, 12:30-14:00

Where: Seminar room, Wetterkreuz 15, 91058 Erlangen

Abstract:

The Antarctic coastline is constantly changing. Three-quarters of the coastline are fringed by ice shelves, the floating extensions of the Antarctic ice sheet. The retreat or disintegration of ice shelves with buttressing forces cause enhanced mass loss of the Antarctic ice sheet increasing global sea level rise.  Continuously tracking ice shelves is challenging because manual mapping cannot keep up with growing satellite archives and automated approaches fail due to the complexity of the Antarctic coastline. Recent advances in deep learning and easy access to high performance computing facilitated a fully-automated framework able to regularly monitor Antarctic ice shelf front dynamics. This presentation explores the unprecedented dense time series of calving front change providing new insights into ice shelf front dynamics and establishes links to ice dynamical and environmental controls on ice shelf extents.

02/05/2023
Event

Invited talk on “Deep-learning-driven estimation of global glacier thickness”

The Institute of Geography at FAU Erlangen-Nürnberg will host an invited talk by Dr. Samuel Cook (Univ. Lausanne).

When: Wednesday, 17.05.2023, 12:30-14:00

Where: Seminar room, Wetterkreuz 15, 91058 Erlangen

Abstract:

I present my ongoing work using the emulator from the Instructed Glacier Model (IGM) (https://github.com/jouvetg/igm) to invert for ice thickness at the 200,000 glaciers in the world outside the polar ice sheets. The basis of the emulator is a convolutional neural network trained on the outputs of full-Stokes simulations of real glaciers. Provided with surface velocities – taken from the new global dataset compiled by Millan et al. (2022) – and surface DEMs, this emulator can invert for thickness at any glacier in the world with a comparable accuracy to traditional full-Stokes inversion, but at a fraction of the computational cost. This allows us to greatly improve our estimates of global glacier volume, vital both for prediction of sea-level rise, but also for local communities in mountainous areas, who often rely on glacier melt for a large proportion of their water resources. I will discuss the rationale and methods behind my work, as well as preliminary results and the problems I’m currently working on.

07/03/2023
Publications

Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard

Research on surge-type glaciers, although they constitute a small percentage of all glaciers, significantly contributes to understanding glacier flow mechanisms. Recent studies have utilized remote sensing techniques to unravel these processes, highlighting the potential of combining high-performance computing and earth observation. While modeling surge events has gained popularity, there is a lack of comprehensive spatial and temporal data on surge timing. To address this, an algorithm has been developed that not only detects surge-type glaciers but also determines the onset of surges. The algorithm relies on time series analysis of glacier surface velocity using Sentinel-1 data, involving seasonal and trend decomposition and outlier detection via the General Studentized Extreme Deviate Test. Cluster analysis is then applied to identify outlier clusters associated with glacier surges. The method’s effectiveness was demonstrated in the Svalbard archipelago between 2015 and 2021, where 18 glacier surges and their timing were successfully identified.

https://www.mdpi.com/2072-4292/15/6/1545

07/02/2023
Allgemein Publications

AMD-HookNet for Glacier Front Segmentation

This article discusses the importance of tracking changes in glacier calving front positions as a means of assessing glacier status. Remote sensing imagery is a valuable tool for this purpose, but manual monitoring for all global calving glaciers is impractical due to time constraints. The article introduces a novel framework called AMD-HookNet, designed for the segmentation of glacier calving fronts in synthetic aperture radar (SAR) images. AMD-HookNet enhances feature representation by leveraging an attention mechanism and interactions between low-resolution and high-resolution inputs. The experiments conducted on a benchmark dataset demonstrate that AMD-HookNet outperforms the current state of the art by achieving a mean distance error of 438 meters to the ground truth, confirming its effectiveness.

https://ieeexplore.ieee.org/document/10044700?source=authoralert

07/02/2023
Publications

A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications

The concept of the sequential sampling is well known and still widely used in industrial automation radar applications due to its hardware simplicity, low cost and low power consumption. However, there is only a limited amount of publications that describe this concept and its variants in detail. This tutorial introduces the typical sequential sampling impulse radar concept step by step and presents several key characteristics, such as correlation properties and SNR considerations. In addition to the system theory, selected applications are presented to illustrate the attractiveness and elegance, but also the limits of this radar concept. The shown applications range from those in industrial automation to radar concepts in the areas of automotive radar, security scanners, biomedical radar systems and ground penetrating radar. The latter application is among others including the analysis of glaciers, e.g. to determine the ice thickness and stratification in order to provide a better understanding of the glacier and its behavior in a changing climate.

The corresponding paper can be found here:

A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications

14/11/2022
Event

Annual Retreat at Josefstal in November 2022

The first annual Retreat within the IDP M3OCCA took place at “Studienzentrum Josefstal” in early November 2022.

During the workshop, all Ph.D. students provided an overview of their individual PhD projects with subsequent discussions.

Additionally, further steps within the IDP M3OOCA program were discussed and defined.

25/06/2022
Event

Kick-Off workshop in Erlangen June 2022

Right to the beginning of the IDP M3OCCA, we organized a “kick-off” workshop at FAU Erlangen in June 2022.

The workshop was the first gathering of all IDP M3OCCA Ph.D. students, including affiliated Ph.D. students. Many subproject supervisors were also around, so the workshop provided the fundament for a fruitful exchange and collaboration between all different institutions involved in the IDP M3OCCA.

 

11/04/2022
SP3 SPs

SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning

Subproject 3.1

Targetting snow drift and refreezing in glacier mass budgets with machine learning

Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning.
This aim links ideally with ongoing snowdrift measurements on Hintereisferner in the frame of a running joint project between FAU and the University of Innsbruck (http://schism-project.info/). It furthermore links very well with our strong background in SMB modelling and developing process-based model parameterizations. We hypothesize, however, that the accuracy of such parameterizations can be increased drastically by including machine learning algorithms and new measurements on snow drift dynamics.

In this subproject, we will explore the associated added value by sensitivity simulations and analyses of the resultant mass and energy balance components. An improved knowledge of the mechanisms in question is also important for the precise estimation of sea level contributions from glaciers measured from altimetry and DEM differencing (SP2.2 and 2.3), since the measured elevation changes have to be converted to mass using a certain density assumption. The SP also links to SP1.2 and SP1.3 where internal structures of the snow and firn cover are mapped.

For specific information on the sub-project please contact: Prof. Dr. Thomas Mölg, Institut für Geographie, Wetterkreuz 15, 91058 Erlangen, T: 09131-85-22633, thomas.moelg@fau.de, https://www.geographie.nat.fau.de/

Co-PIs: L. Nicholson, R. Prinz (Univ. Innsbruck), R. Hock (Univ. Oslo)

Get to know our project affiliated PhD students

Manuel Saigger

manuel.saigger@fau.de

Want to learn more about Manuel’s research? Click here to access a short video!
(the webcam video is from www.foto-webcam.eu)

My name is Manuel Saigger and within the IDP MOCCA I will be working on the influence of snow drift and internal refreezing on the glacier mass balance. Before I started working in Erlangen,  I studied atmospheric sciences at the University Innsbruck. There I mainly focused on weather forecasting and weather simulations in alpine terrain and got really fascinated by the complex nature of flow fields in the surrounding of mountains. These complex wind patterns can in return lead to very complex patterns in snow accumulation, for example by redistribution of the snow.

Here my project comes into play. Because despite possibly being very important, this redistribution is not reflected in most glacier mass balance models, which on the one hand creates uncertainty in these models, but on the other hand gives us the opportunity to work on this problem and learn more about these fascinating processes.