Date of Award:

5-2026

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mathematics and Statistics

Committee Chair(s)

Brennan Bean

Committee

Brennan Bean

Committee

Yan Sun

Committee

Abbie Liel

Abstract

Heavy snow accumulation on rooftops is a serious structural risk in cold climates, and understanding how much snow builds up on different types of roofs is essential for safe building design. Currently, most data on roof snow loads comes from small, labor-intensive field surveys that cover only a handful of buildings at a time. This results in far too few measurements of buildings to draw confident conclusions about how snow behaves across communities. This thesis develops and demonstrates a new automated approach for measuring roof snow accumulation and extracting key building characteristics across thousands of buildings at once using airborne laser scanning data (LiDAR) collected over Fairbanks, Alaska. By comparing laser scans taken before and after snowfall, it is possible to estimate how much snow has accumulated on individual rooftops. The methods developed here automatically identify the usable roof area of each building, estimate building height, and determine roof steepness, all of which influence how snow loads develop and how they should be accounted for in structural design. Applied to Fairbanks, the methodology could drastically increase the number of buildings available for roof snow load analysis compared to what traditional snow survey methods have been able to provide. Extracting meaningful snow load statistics ultimately requires laser scanning data collected on multiple dates throughout the winter season, rather than on a single day. The contribution of this work is the complete analytical framework, which includes an open-source software package that makes large-scale roof snow load analysis tractable once that kind of data becomes available. The methods are designed to be transferable to other cold-region communities where similar data exist or could be collected.

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