Resilient AI Systems for Polar Glacier Monitoring

The primary objective of this study is to utilize resilient AI systems to comprehensively comprehend the glacier calving process and its environmental impact through long-to-short-term monitoring data.

Despite significant advancements, our understanding of glacier mechanics, particularly the processes leading to mass loss through calving, remains incomplete.

Current non-satellite monitoring methods face limitations due to weather constraints and logistical challenges, hindering comprehensive data acquisition.

Moreover, the unconventional nature of these datasets limits our ability to automate data processing, with only a few machine learning algorithms currently in use. This project aims to develop AI/ML methods for detecting and comprehending glacier calving mechanisms.

Additionally, I will reconfigure existing drone platforms to collect samples and visual data, enabling the creation of a more detailed dataset on frontal ice loss.