A Benchmark Dataset for Radio Echo Sounding Data - Dreier, Marcel Nicolas; Gourmelon, Nora; Koch, Moritz; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (European Geosciences Union 2024, 2024)
Melting glaciers and ice sheets contribute significantly to the global sea level rise, posing an existential threat to coastal regions. For that reason, many expeditions collect radio echo sounding data to track, model, and predict the changes in ice mass and thickness. However, manually analyzing the data collected to determine the thickness of the ice sheet and mountain glaciers is a time-consuming task, as the collected data often extends over tens to hundreds of kilometers. Hence, the development and utilization of automated and semi-automated tools have risen in popularity to alleviate the problem. Especially machine learning-based approaches show promising results as they can capture complex relationships in the data. However, such methods require large labeled datasets to achieve their full potential. Thus, we are currently gathering and cleaning data that will be released as a benchmark dataset for radio echo sounding data to train, test, and compare different approaches. In detail, the dataset will consist of 2D radar data with georeferenced labels for the position of the air-ice and ice-bedrock interface. We plan to incorporate radar data from multiple sources since the underlying radar system and expedition area play a significant role in depicting the data and the general difficulty of the task. That way, the performance of a system can be fairly assessed with the benchmark dataset without the evaluation results being skewed by external factors. We will further ensure a fair comparison by dividing the data of every source in an independent training, validation, and test set. This division will also allow us to use the benchmark dataset to train and compare systems specialized on only a single data source. Thus, future work can also utilize our benchmark dataset to develop systems specialized on only a single data source. Lastly, we will release a baseline model alongside the benchmark dataset to demonstrate its practicality. Thereby giving an initial point of reference that future work can compare to. <

Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series-An Example from Svalbard - Koch, Moritz; Seehaus, Thorsten; Friedl, Peter; Braun, Matthias (Remote Sensing, 2023)
Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase.
CRIS-Team WoS Importer:2023-04-21