  This work presents the first quantitative study of alignment errors between
small uncrewed aerial systems (sUAS) geospatial imagery and a priori building
polygons and finds that alignment errors are non-uniform and irregular. The
work also introduces a publicly available dataset of imagery, building
polygons, and human-generated and curated adjustments that can be used to
evaluate existing strategies for aligning building polygons with sUAS imagery.
There are no efforts that have aligned pre-existing spatial data with sUAS
imagery, and thus, there is no clear state of practice. However, this effort
and analysis show that the translational alignment errors present in this type
of data, averaging 82px and an intersection over the union of 0.65, which would
induce further errors and biases in downstream machine learning systems unless
addressed. This study identifies and analyzes the translational alignment
errors of 21,619 building polygons in fifty-one orthomosaic images, covering
16787.2 Acres (26.23 square miles), constructed from sUAS raw imagery from nine
wide-area disasters (Hurricane Ian, Hurricane Harvey, Hurricane Michael,
Hurricane Ida, Hurricane Idalia, Hurricane Laura, the Mayfield Tornado, the
Musset Bayou Fire, and the Kilauea Eruption). The analysis finds no uniformity
among the angle and distance metrics of the building polygon alignments as they
present an average degree variance of 0.4 and an average pixel distance
variance of 0.45. This work alerts the sUAS community to the problem of spatial
alignment and that a simple linear transform, often used to align satellite
imagery, will not be sufficient to align spatial data in sUAS orthomosaic
imagery.
