CRASAR/CRASAR-U-DROIDs

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 and building polygon alignment. For specific documentation describing the data in this repository, please reference the publications section below.

This dataset contains 265 orthomosaics containing 122502 views of 21716 building polygons collected from 10 different disasters and sourced from Satellites, Crewed Aircraft, and Drones; totaling 70.6 gigapixels of imagery and covering 68km^2. Building polygons were sourced from Microsoft's US Building Footprints Dataset [1], and in some cases, building polygons were added manually by the authors. Each building polygon has been annotated using the Joint Damage Scale [2] and translationally aligned for model training. The dataset has been split into test and train at the disaster level, with 6 disasters in the train set and 4 disasters in the test set. A validation set is intentionally not provided, and model validation is left to the user. A summary of the dataset, grouped by disaster and ordered by area, is included below for reference.

Disaster	Area (km^2)	Gigapixels	Building Labels	Orthomosaics	Test or Train
Hurricane Ian	32.66517523	33.19155902	100351	200	Train
Mayfield Tornado	8.422144185	9.698707535	2028	3	Test
Kilauea Eruption	5.751864646	1.121020488	382	3	Train
Hurricane Idalia	5.686794335	1.095231308	4636	12	Test
Hurricane Ida	5.139696352	6.976915134	2068	9	Train
Hurricane Michael	3.617024461	9.567229047	6859	12	Test
Hurricane Harvey	2.596253635	5.128525423	5546	17	Train
Hurricane Laura	2.341867225	1.456463	500	3	Train
Mussett Bayou Fire	1.714575473	2.164129413	128	5	Test
Champlain Towers Collapse	0.041536185	0.246084846	4	1	Train
Total	67.97693173	70.64586393	122502	265	N/A

Dataset Format

At the top level, the dataset contains a statistics.csv file, with summary statistics of the dataset, and two folders, train and test. 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: 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. A complete description of the data set schema and annotation schemas can be found in the format directory of this dataset.

Building Damage Assessment (BDA)

A sample of a building damage assessment JSON file is as follows...

[
  {
    "view_id": "a75d08c2bcaf852707c66464f5a4abc6",
    "building_id": "3441f4d81904563bba38da0fd8cea8b4",
    "label": "major damage",
    "source": "Microsoft",
    "boundary": "0827-B-02.geo.tif",
    "filename": "0827-B-02.geo.tif.json",
    "jds_version": "1.1",
    "payload_version": "2.0"
    "EPSG:4326": [
      {
        "lat": 30.092885,
        "lon": -93.728311
      },
      {
        "lat": 30.092886,
        "lon": -93.7284
      },
      {
        "lat": 30.092953,
        "lon": -93.7284
      },
      {
        "lat": 30.092885,
        "lon": -93.728311
      }
    ],
    "pixels": [
      {
        "x": 1999,
        "y": 13267
      },
      {
        "x": 1776,
        "y": 13263
      },
      {
        "x": 1777,
        "y": 13070
      },
      {
        "x": 1999,
        "y": 13267
      }
    ]
  },
  ...
]

Each JSON file contains a list where each entry is a labeled view of a building polygon and contains the following information...

• The "view_id" field is a string that uniquely identifies this specific view of the building. As the same building may appear in multiple orthomosaics with different labels, the view_id uniquely identifies this specific view and label of the building.
• The "building_id" field is a string that uniquely identifies this building. As the same building may appear in multiple orthomosaics, this "building_id" can be used to correlate buildings across orthomosaics.
• The "label" field corresponds to the values of the Joint Damage Scale (JSD). The possible options are "no damage", "minor damage", "major damage", "destroyed", "un-classified", and "obscured". While the JDS does not contain the "obscured" label, this label was added to handle cases where buildings were obscured by clouds, smoke, trees, or other buildings.
• The "source" field describes the provenance of the building polygon. The possible options are "Microsoft," indicating the building polygon was sourced from the Microsoft Building Footprints dataset, and "custom," indicating the polygons were manually added by the authors.
• The "boundary" field describes the orthomosaic imagery that was used to define a boundary that determined the inclusion of this polygon in the dataset.
• The "filename" field describes the json file in which you should find this entry.
• The "jds_version" field describes the version of JDS that is expected to appear in the "label" field.
• The "payload_version" field describes the version of the json payload. Previous versions of the dataset did not contain a "payload_version" and this field is used to ensure consistent parsing of dataset content across revisions.
• 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 for BDA & RDA

A sample of the alignment adjustment JSON file is as follows...

[[[4739.728, 4061.728], [4542.137, 3962.933]], ... ]

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 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].

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. 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, and the data found at commit 58f0d5ea2544dec8c126ac066e236943f26d0b7e.
3. [RO-MAN'25] Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters. 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. 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.
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.
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.

Accessing Specific Commits

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.