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README.md

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 This repository contains the CRASAR-U-DROIDs dataset. This is a dataset of orthomosaic images with accompanying labels for building damage assessment. The data contained here has been documented in existing academic papers described below...
 
-1) [\[FAccT'25\] Now you see it, Now you don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](). This work describes the label disagreement phenomenon observed between drone and satellite imagery. To replicate the results of this paper, please see commit 58f0d5ea2544dec8c126ac066e236943f26d0b7e.
+1) [\[FAccT'25\] Now you see it, Now you don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](). This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02\% label disagreement and significantly different distributions between the two sources, which presents risks and potential harms during the deployment of machine learning damage assessment systems. To replicate the results of this paper, please see commit 58f0d5ea2544dec8c126ac066e236943f26d0b7e.
 2)  [Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters](https://arxiv.org/abs/2405.06593). 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. To replicate the results from this paper, please see commit ae3e394cf0377e6e2ccd8fcef64dbdaffd766434.
 3) [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://arxiv.org/abs/2407.17673). This work represents the initial release of the CRASAR-U-DROIDs dataset and was the first description of the work. To replicate the results from this paper, please see commit ae3e394cf0377e6e2ccd8fcef64dbdaffd766434.