GlacierNet: A Deep Learning Architecture for Debris-Covered Glacier Mapping
The global temperature has been continuously increasing over the past decades. The effect of temperature increase can directly affect the health, dynamics, and processes of alpine glaciers. In this research, the convolutional neural network (CNN), which is a deep learning, feed-forward neural network, is applied to the Landsat era satellite images for automated mapping of debris-covered glaciers. Our preliminary results indicate high accuracy in glacier mapping, a major step in developing a fully automated methodology for glacier mapping.
Umesh K Haritashya, K. Asari Vijayan
Primary Advisor's Department
Electrical and Computer Engineering
Stander Symposium poster
"GlacierNet: A Deep Learning Architecture for Debris-Covered Glacier Mapping" (2019). Stander Symposium Posters. 1518.