Principal Investigator Timothy Pasch highlights that the advanced cyberinfrastructure will enable geospatial forecasting up to 20 years, which is vital for Arctic infrastructure investment, national preparedness, and reducing costs.

The project aims to improve situational awareness, real-time monitoring, and predictive analytics for Arctic operations, addressing challenges from climate change and harsh environments.

This system will process large datasets, including satellite imagery, LiDAR scans, and geospatial data, with secure data transmission to the San Diego Supercomputing Center's EXPANSE Supercomputer, supported by NSF credits.

The University of North Dakota is leading a collaborative effort to develop an AI-powered Arctic research system called the Arctic Knowledge-Based System (A-KBS), which leverages a high-performance Kubernetes cluster named 'Helmsman' to support decision-making in extreme cold environments.

Additional partners, such as Virginia Tech, are contributing to infrastructure resilience, soil stability, geospatial forecasting, and environmental monitoring efforts in the Arctic.

He also emphasizes the importance of leveraging AI, Earth-scale data science, and remote sensing technology as the Arctic gains strategic significance for global stability and sovereignty.

The project includes a $100,000 subaward for collaborative research with SUNY Stony Brook on developing sustainable, freeze-resistant materials like hydrogels to mitigate cold-related infrastructure damage.

UND’s team comprises graduate students, faculty, and research engineers working on computational development, data collection, and system integration to enhance capabilities.

Supported by a contract from the US Army Corps of Engineers R&D Center, this initiative marks UND’s first deployment of supercomputing capabilities via a Kubernetes cluster.

It reflects a national effort to increase Arctic resilience through cutting-edge AI, Earth-scale data science, and remote sensing technologies.

Ultimately, the initiative seeks to enhance resilience and stability in Arctic operations through advanced AI-driven modeling and long-term data analysis.

Co-leaders include Naima Kaabouch of the UND AI Research Center and Aaron Bergstrom, who manages UND’s High Performance Computing Cluster.

The Arctic Knowledge-Based System (A-KBS) will utilize real-time data analytics and long-term forecasting to boost situational awareness and operational resilience in the Arctic.