Deep learning-based rock glacier mapping using Earth observation data

Rock glaciers are tongue-shaped complex landforms that indicate current or past permafrost conditions. They are commonly found in high-latitude and/or high-elevation environments and consist of poorly sorted angular debris and ice-rich sediments formed by gravity-driven creep. In the Austrian Alps, it is estimated that over 5700 rock glaciers exist (Kellerer-Pirklbauer et al., 2022). Knowing the location, extent and characteristics of rock glaciers is important for several reasons. These include estimating their hydrological importance as a water resource (e.g., for alpine huts) and assessing the geohazard potential because of the destabilisation of rock glaciers due to climate change. Unlike other cryosphere features, such as snow and glaciers, rock glaciers are spectrally inseparable from the surrounding terrain. This makes them difficult to automatically detect and delineate from Earth observation (EO) data. As a result, rock glaciers are usually mapped by labour-intensive, subjective manual interpretation of EO data. This often leads to inhomogeneous, incomplete, and inconsistent mapping. Therefore, there is a need for automated and efficient methods to map rock glaciers. This can be achieved by using globally applicable satellite data sets such as Sentinel-2.   

Modern machine learning methods, such as deep learning (DL), provide new opportunities to automate mapping tasks and address the challenges of detecting rock glaciers from EO data. However, research on DL-based rock glacier mapping remains limited, and there is no consensus on the best-suited parameters for this application. In addition, features with surface textures similar to rock glaciers, such as landslides, avalanche deposits, or fluvial deposits, may be misclassified by DL models. Hence, a thorough investigation of the DL model architectures and input data types is necessary to determine the most effective approach for mapping rock glaciers. In the project “ROGER - EO-based rock glacier mapping and characterisation”, we test different DL models (e.g. Unet, DeepLABV3) with different settings (backbones, input layers (including optical imagery and DEM-derived information)) to identify the most suitable model for rock glaciers delineation in Austria. We evaluate the performance, robustness, and reliability of the different DL models for automated EO-based mapping of rock glaciers in different study areas in Austria, and quantify the accuracy of the results in comparison with reference data.

Through our study, we aim to make a substantial contribution to cryospheric research by evaluating methods for the automated identification of rock glaciers, thereby enhancing our understanding of the potential of DL to efficiently map complex natural phenomena using EO data. The results will also contribute to increase the trustworthiness of DL methods, which is critical for various applications and particularly in communicating and explaining results to stakeholders and decision makers. 

 

Kellerer-Pirklbauer, A., Lieb, G.K., Kaufmann, V. (2022). Rock Glaciers in the Austrian Alps: A General Overview with a Special Focus on Dösen Rock Glacier, Hohe Tauern Range. In: Embleton-Hamann, C. (eds) Landscapes and Landforms of Austria. World Geomorphological Landscapes. Springer, Cham.