Update Papers.md

Papers.md

@@ -4,5 +4,5 @@ The following papers exist that describe the dataset and its intended uses...
 2) [\[FAccT'25\] Now you see it, Now you don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](https://arxiv.org/abs/2505.08117). 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 use the source code located [here](https://github.com/TManzini/NowYouSeeItNowYouDont/), and the data found at commit [58f0d5ea2544dec8c126ac066e236943f26d0b7e](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/58f0d5ea2544dec8c126ac066e236943f26d0b7e).
 3) [\[RO-MAN'25\] 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](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).
 4) [\[RO-MAN'25\] Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene](https://arxiv.org/pdf/2506.15890). This work presents the research directions and challenges that were encountered from the operational deployment of ML models trained on the CRSAR-U-DROIDs dataset. To find the data used to train the models used in this work, please see commit [ae3e394cf0377e6e2ccd8fcef64dbdaffd766434](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).
-5) [\[IAAI'26\] Deploying Rapid Damage Assessments with sUAS Imagery in Disaster Response Operations](). The paper is a summative look at the building damage assessment effort spanning data annotation, model training, operator training, and deployment. To find the data used to train the models used in this work, please see commit [ae3e394cf0377e6e2ccd8fcef64dbdaffd766434](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).
-6) [\[AAAI'26\] A Benchmark Dataset and Baseline Models for Spatially Aligned Road Damage Assessment in sUAS Disaster Imagery](). This paper introduces the Road Damage Assessment Task to the sUAS imagery in the CRASAR-U-DROIDs dataset. To find the data used to train the models used in this work, please see commit [59587a754077528b01217e33764dfa3822e44238](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/59587a754077528b01217e33764dfa3822e44238).
+5) [\[IAAI'26\] Deploying Rapid Damage Assessments with sUAS Imagery in Disaster Response Operations](https://arxiv.org/pdf/2511.03132). The paper is a summative look at the building damage assessment effort spanning data annotation, model training, operator training, and deployment. To find the data used to train the models used in this work, please see commit [ae3e394cf0377e6e2ccd8fcef64dbdaffd766434](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).
+6) [\[AAAI'26\] A Benchmark Dataset and Baseline Models for Spatially Aligned Road Damage Assessment in sUAS Disaster Imagery](https://arxiv.org/pdf/2407.17673). This paper introduces the Road Damage Assessment Task to the sUAS imagery in the CRASAR-U-DROIDs dataset. To find the data used to train the models used in this work, please see commit [59587a754077528b01217e33764dfa3822e44238](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/59587a754077528b01217e33764dfa3822e44238).