The CRASAR sUAS [D]isaster [R]esponse [O]verhead [I]nspection [D]ata[s]et

Read the full paper for more details: CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery

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 Footprint's 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 summary of the dataset, grouped by disaster and ordered by area, is included below for reference.

Disaster	Area (km^2)	Gigapixels	Building Polygons	Orthomosaics	Test or Train
Hurricane Ian	32.66517523	30.7383172	14326	25	Train
Mayfield Tornado	8.422144185	9.698707535	2036	3	Test
Kilauea Eruption	5.751864646	1.121020488	385	3	Train
Hurricane Idalia	5.686794335	0.351551451	782	2	Test
Hurricane Ida	5.139696352	6.743893458	1095	5	Train
Hurricane Michael	3.617024461	9.450281054	1145	2	Test
Hurricane Harvey	2.596253635	5.075368273	1336	4	Train
Hurricane Laura	2.341867225	1.4456527	478	2	Train
Mussett Bayou Fire	1.714575473	2.164129413	129	5	Test
Champlain Towers Collapse	0.041536185	0.246084846	4	1	Train
Total	67.97693173	67.03500642	21716	52	N/A

Dataset Structure

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 folders imagery (which contains all of the geo.tif files) and annotations. The annotations folder then contains two other folders: alignment_adjustments, and building_damage_assessment. Each of these folders contains JSON files containing the annotations for both building damage assessment and the translational alignments necessary to align the building polygons with the imagery.

Building Damage Assessment

A sample of the building damage assesssment JSON file is as follows...

[{"source": "custom", "id": "8194baa7a68e2cbfe6506c0f6c00a785", "label": "major damage", "pixels": [{"x": 5823, "y": 6310}, ...], "EPSG:4326": [{"lat": 25.87311942079238, "lon": -80.12125843985305}, ...]}, ...]

Each JSON file is a list of dictionaries, where each dictionary defines a building polygon and its metadata.

The "source" field describes the provenance of the building polygon. The possible options are "Microsoft" indicating the building polygon was sourced from the Microsot Building Footprints dataset, and "custom" indicating the polygons were manually added by the authors.
The "id" field is a unique string id for each building polygon.
The "label" field corresponds to the values of the Joint Damage Scale. The possible options are "no damage", "minor damage", "major damage", "destroyed", and "un-classified".
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

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 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 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.