Update README.md

README.md

@@ -5,12 +5,7 @@ size_categories:
 ---
 # The CRASAR sUAS \[D\]isaster \[R\]esponse \[O\]verhead \[I\]nspection \[D\]ata\[s\]et
 
-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) [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.
+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 is described in [this document](https://arxiv.org/abs/2407.17673).
 
 This dataset contains 52 orthomosaics containing 21716 building polygons collected from 10 different disasters, totaling 67 gigapixels of imagery. 
 Building polygons were sourced from Microsoft's US Building Footprints Dataset \[[1](https://github.com/microsoft/USBuildingFootprints)\], and in some cases, building polygons were added manually by the authors. 
@@ -121,4 +116,13 @@ The second list represents a set of two points, forming a line, that describes t
 Each translational adjustment starts with the position in the unadjusted coordinate space that needs to be moved to the following position in order to align the building polygons.
 These translational adjustments are applied to the building polygons by applying the nearest adjustment to each building polygon. 
 Functionally, this forms a vector field that describes the adjustments for an entire orthomosaic.
-This process is described in detail in [3](https://arxiv.org/abs/2405.06593).
+This process is described in detail in [3](https://arxiv.org/abs/2405.06593).
+
+## Publications & Documentation
+
+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.