We organized a one-day workshop on ‘How to give a good talk’.
The workshop covered various topics from Stage fright over Body Language to Humor in talks. The participants benefited a lot from the intensive feedback they received from their colleagues and the trainer.
Thank you again, Nae, for the great workshop!
Knowledge about permafrost distribution is critical for the assessment of rock mass stability. By installing rock surface temperature loggers we aim to create a model to explore the distribution of permafrost in the Ötztal Alps. Solar incoming radiation and air temperature are the main drivers of permafrost evolution. Therefore we picked diverse locations in height, aspect and slope for installing the loggers.
The field work took place on a wonderful sunny day on 6th of September 2023. We are happy that everything worked out as planned and we returned home from the mountains in a safe way with good new stories on our shoulders.
The study emphasizes the importance of understanding marine-terminating glacier dynamics in glacier projections. Deep learning methods can automate the extraction of calving front positions from satellite imagery, reducing manual effort. The “CaFFe” dataset, which includes annotated calving fronts in Synthetic Aperture Radar (SAR) imagery, offers a standardized benchmark for evaluating deep learning techniques in this area. Researchers can use CaFFe to assess the performance of upcoming deep learning models and identify promising research directions. A leaderboard of models can be found at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.
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