# Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters

arXiv: https://arxiv.org/abs/2405.06593 · HTML: https://mlsanity.com/paper/2405.06593/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 9 of 9)

- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](https://mlsanity.com/paper/2006.16479/) (2020)
- [Correcting rural building annotations in OpenStreetMap using convolutional neural networks](https://mlsanity.com/paper/1901.08190/) (2019)
- [Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event](https://mlsanity.com/paper/2306.12589/) (2023)
- [Quantitative Data Analysis: CRASAR Small Unmanned Aerial Systems at Hurricane Ian](https://mlsanity.com/paper/2308.14577/) (2023)
- [Pix2Streams: Dynamic Hydrology Maps from Satellite-LiDAR Fusion](https://mlsanity.com/paper/2011.07584/) (2020)

## Built on by (most cited first, 3 of 3)

- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response](https://mlsanity.com/paper/2511.03132/) (2025)
- [A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery](https://mlsanity.com/paper/2512.12128/) (2025)

## Abstract (arXiv)

This work presents the first quantitative study of alignment errors between small uncrewed aerial systems (sUAS) georectified imagery and a priori building polygons and finds that alignment errors are non-uniform and irregular, which negatively impacts field robotics systems and human-robot interfaces that rely on geospatial information. There are no efforts that have considered the alignment of a priori spatial data with georectified sUAS imagery, possibly because straight-forward linear transformations often remedy any misalignment in satellite imagery. However, an attempt to develop machine learning models for an sUAS field robotics system for disaster response from nine wide-area disasters using the CRASAR-U-DROIDs dataset uncovered serious translational alignment errors. The analysis considered 21,608 building polygons in 51 orthomosaic images, covering 16787.2 Acres (26.23 square miles), and 7,880 adjustment annotations, averaging 75.36 pixels and an average intersection over union of 0.65. Further analysis found no uniformity among the angle and distance metrics of the building polygon alignments, presenting an average circular variance of 0.28 and an average distance variance of 0.45 pixels2, making it impossible to use the linear transform used to align satellite imagery. The study's primary contribution is alerting field robotics and human-robot interaction (HRI) communities to the problem of spatial alignment and that a new method will be needed to automate and communicate the alignment of spatial data in sUAS georectified imagery. This paper also contributes a description of the updated CRASAR-U-DROIDs dataset of sUAS imagery, which contains building polygons and human-curated corrections to spatial misalignment for further research in field robotics and HRI.