Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study

• Published in: Remote Sensing
• Main Authors: Mohajerani, Yara, Wood, Michael, Velicogna, Isabella, Rignot, Eric
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2019
• Subjects: Climate Action; calving front; image segmentation; U-Net; convolutional neural network; machine learning; Greenland; Classical Physics; Physical Geography and Environmental Geoscience; Geomatic Engineering; Antarc*; Antarctica; glacier; Jakobshavn; Kangerlussuaq
• Online Access: https://escholarship.org/uc/item/9pj5t0zx; https://escholarship.org/content/qt9pj5t0zx/qt9pj5t0zx.pdf; https://doi.org/10.3390/rs11010074

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and pai...