Presenter(s)

    Zhiyuan Xie

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    Description

    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.

    Publication Date

    4-24-2019

    Project Designation

    Independent Research

    Primary Advisor

    Umesh K. Haritashya, Vijayan K. Asari

    Primary Advisor's Department

    Electrical and Computer Engineering

    Keywords

    Stander Symposium project

    GlacierNet: A Deep Learning Architecture for Debris-Covered Glacier Mapping

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