Update README.md

README.md

@@ -122,7 +122,10 @@ This process is described in detail in [3](https://arxiv.org/abs/2405.06593).
 
 The following papers exist that describe the dataset and its intended uses...
 
-1) [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 paper presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building damage assessment and spatial alignment collected from small uncrewed aerial systems (sUAS) geospatial imagery. To replicate the results from this paper, please see commit ae3e394cf0377e6e2ccd8fcef64dbdaffd766434.
-2) [\[FAccT'25\] 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 use the source code located [here](https://github.com/TManzini/NowYouSeeItNowYouDont/), and the data found at commit 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.
-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.
+1) [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 paper presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building damage assessment and spatial alignment collected from small uncrewed aerial systems (sUAS) geospatial imagery. To replicate the results from this paper, please see commit [ae3e394cf0377e6e2ccd8fcef64dbdaffd766434](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).
+2) [\[FAccT'25\] 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 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).
+
+## Accessing Specific Commits
+To Access a specific hash simply add the has after https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ in the URL. For example: [https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434](https://huggingface.co/datasets/CRASAR/CRASAR-U-DROIDs/tree/ae3e394cf0377e6e2ccd8fcef64dbdaffd766434).