Mapping of Glaciers in the Poiqu Basin (Central Himalaya) Using U-Net and Transfer Learning

Monitoring of glaciers is crucial as they are an important source of freshwater, an indicator of global warming, and a contributor to sea level rise. Accurate delineation of glaciers plays a crucial role in glacier monitoring and remote sensing is the most appropriate tool to map glaciers.

Existing glacier inventories have shortcomings such as unavailability in recent years and data quality. Traditional glacier mapping methods using remote sensing often rely on spectral band ratio techniques or manual digitizing. However, glacier boundaries achieved from manual digitizing are highly affected by human errors. Moreover, in the band ratio technique challenges arise in mapping debris-covered glaciers as traditional optical methods fail to distinguish debris-covered ice from surrounding rock due to their spectral similarities. Therefore, automatic mapping of glaciers is still challenging.

Advanced deep learning methods have demonstrated significant advancements in automatic glacier mapping. However, the potential of state-of-the-art deep learning methods in glacier mapping has not yet been fully explored. When it comes to deep learning, one of the challenges is the amount of training data. With the low amount of training data, the results won't be of the desired accuracy. However, it is still possible to obtain good results using a lower amount of training data and the transfer learning technique.

This study focuses on glacier mapping in Poiqu Basin (Central Himalaya), using U-Net and transfer learning. To this purpose, Sentinel-2 images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model are deployed.

The results indicate that transfer learning leads to considerably better results than training the deep learning network from scratch. Moreover, trying different backbones does not considerably affect the results. This study highlights the efficiency of the transfer learning technique, emphasizing its potential and effectiveness in regions with limited training data.