Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters
<|reference_start|>Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters: 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.<|reference_end|>
@article{manzini2024non-uniform,
    title={Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area
  Disasters},
    author={Thomas Manzini, Priyankari Perali, Raisa Karnik, Mihir Godbole, Hasnat
  Abdullah, Robin Murphy},
    journal={arXiv preprint arXiv:2405.06593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06593},
    primaryClass={cs.CV}
}