LAUSR

Abstract Remotely sensed data provide many opportunities for enhancing our understanding of the built and natural environment. Representations of the urban landscape from light detection and ranging (LiDAR) sensors and digital orthophotography from unmanned aerial systems (UAS) are quickly becoming essential for examining and maintaining infrastructure systems, estimating risk from extreme events, and improving urban sustainability. This includes community efforts toward energy resilience and the development of alternative energy systems, such as solar and wind. While LiDAR provides the means to model key characteristics of the urban landscape for solar energy planning, including slope, aspect and elevation, issues of spatial uncertainty and error persist in LiDAR data and have the potential to reduce the fidelity of solar energy assessments. In this paper, we use extremely high-resolution UAS data to improve solar energy audits and mitigate uncertainties associated with LiDAR data. The results suggest improvements in aggregate irradiation estimates by as much as 36 % when using digital orthophotos from a UAS when compared to LiDAR. This paper concludes with a detailed discussion of potential strategies for improving solar energy estimates for both researchers and practitioners.