Update README.md

README.md

@@ -32,11 +32,11 @@ At the top level, the dataset contains a statistics.csv file, with summary stati
 Each folder has imagery (which contains all of the geo.tif files) and annotations.
 The imagery folder contains four folders for four sources of imagery: UAS, UAS_DSM, SATELLITE, and CREWED. All imagery is provided as *.geo.tif files at their maximum available resolution. UAS_DSM are digital surface maps (DSMs) generated by the UAS mapping software. Not all runs of the mapping software resulted in valid DSMs, and so only some sUAS imagery have parallel DSMs.
 The annotations folder then contains one folder for each source of imagery (and therefore labels): UAS, SATELLITE, and CREWED. These folders contain the imagery-derived labels from the imagery associated with each of the imagery sources. UAS_DSM imagery was not labeled.
-These folders contain two groups of data: alignment_adjustments, and building_damage_assessment.
+These folders contain two groups of data: building_alignment_adjustments, and building_damage_assessment.
 These two groups of data contain JSON data that represent the annotations for both building damage assessment and the translational alignments necessary to align the building polygons with the imagery.
 These two data sources are discussed below.
 
-### Building Damage Assessment
+### Building Damage Assessment (BDA)
 A sample of a building damage assessment JSON file is as follows... 
 ```json
 [
@@ -98,18 +98,18 @@ Each JSON file contains a list where each entry is a labeled view of a building
 - The "pixels" field corresponds to the coordinates of the building polygon in the pixel coordinate space of the orthomosaic.
 - The "EPSG:4326" field corresponds to the coordinates of the building polygon in the EPSG:4326 coordinate space.
 
-### Alignment Adjustments
+### Alignment Adjustments for BDA & RDA
 A sample of the alignment adjustment JSON file is as follows...
 ```json
 [[[4739.728, 4061.728], [4542.137, 3962.933]], ... ]
 ```
 
-Each JSON file is a list of lines with a length of two, each defined by a 2d coordinate corresponding to an x,y pixel coordinate in the orthomosaic.
-The first list represents a list of all the alignment adjustments for the given orthomosaic.
-The second list represents a set of two points, forming a line, that describes the translational adjustment needed to bring the nearby building polygons into alignment with the imagery.
+Each JSON file is a list of lines, each with a length of two, defined by a 2D coordinate corresponding to an x, y pixel coordinate in the orthomosaic.
+The first list represents all the alignment adjustments for the given orthomosaic.
+The second list represents a set of two points, forming a line, that describes the translational adjustment needed to bring the nearby building polygons or road line vertices into alignment with the imagery.
 
 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. 
+These translational adjustments are applied to the building polygons and road line vertices by applying the nearest adjustment to each building polygon or road line vertex. 
 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).
 
@@ -121,6 +121,8 @@ 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).
 
 ## 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).
+To access a specific hash, simply add the hash 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).