Natural Resource Supply Chain Optimization using Aerial Imagery Interpreted with Machine Learning Methods

The University of Montana and the University of Alaska Anchorage are partnering to conduct scientific research that directly supports the needs of their natural‑resource‑based economies, focusing on snow water resources, wildland fire management, and abandoned oil well monitoring. Across all three areas, the project uses a shared technical approach: unmanned aerial systems (UAS or drones) collect high‑quality imagery and environmental measurements, and machine learning techniques are then applied to interpret these large, complex datasets and answer key scientific questions. This combination of drone‑based data collection and machine learning not only advances research but also builds critical workforce skills for the future. The project emphasizes paid, credit‑bearing internships with local industry partners, prioritizing student retention and ensuring that Indigenous, low‑income, and rural community members are fully included. Scientifically, the work aims to improve assessments of mountain snowpack as a vital economic resource, deepen understanding of the physical and ecological processes that drive wildfire behavior, and enhance the detection and mapping of abandoned or improperly sealed oil and gas wells to support mitigation and reclamation. By centering locally relevant research, the collaboration strengthens ties with regional industries and highlights UAS and ML as essential tools for both commercial and academic innovation. The project also incorporates social science research to examine the broader social and economic impacts of these emerging technologies, recognizing their long‑term implications for workers and communities.