AICE: AI Solutions for the Arctic & Antarctic Discovery

• Main Author: Rahnemoonfar, Maryam
• Format: Conference Object
• Language: unknown
• Published: ESIP 2020
• Subjects: Artificial Intelligence and Image Processing; FOS Computer and information sciences; 80104 Computer Vision; Arctic; Antarctic; Antarc*; Center for Remote Sensing of Ice Sheets (CReSIS)
• Online Access: https://dx.doi.org/10.6084/m9.figshare.13341836.v1; https://esip.figshare.com/articles/presentation/AICE_AI_Solutions_for_the_Arctic_Antarctic_Discovery/13341836/1

The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS and Operation IceBridge mission at NASA. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge.