Deep Learning Based Glacial Lake Mapping in the Austrian Alps

Climate change causes significant glacial retreat in the Austrian Alps. Glacial retreat is linked to an increase in the number and size of glacial lakes. The emergence and growth of glacial lakes threatens alpine infrastructure and can cause glacial lake outburst floods (GLOF's). Therefore, it is important to monitor the spatio-temporal evolvement of glacial lakes. Remote sensing provides possibilities for cost-effective glacial lake monitoring. Besides, in recent years various deep learning-based models have been introduced as effective tools for computer vision tasks, including semantic segmentation.
In this study, a Unet-based semantic segmentation model architecture has been implemented, trained and assessed on a custom training dataset.
The dataset is based on an inventory of Austrian glacial lakes in 2015 and Sentinel-2 imagery and contains 386 image chips, 270 for training and 116 for testing, each measuring 512 by 512 pixels and including at least one glacial lake from the inventory.

The semantic segmentation model has been applied to a time series of Sentinel 2 imagery from 2015 to 2025 in order to create a time series of glacial lake maps. The final results will be used to examine whether the number of glacial lakes has increased in recent years and to examine spatio-temporal trends in glacial lake evolution. Potential impacts of the observed developments on (hiking) infrastructure will also be assessed.

The geospatial workflow is implemented using open-source tools and freely available datasets.