List of Datasets

UAV

DDOS: The Drone Depth and Obstacle Segmentation Dataset

Aerial depth estimation and obstacle segmentation

Synthetic aerial RGB sequences with depth, semantic segmentation, flow, normals and drone state, emphasizing thin obstacles and varied weather.

Metadata: Complete Metadata needs review

Primary places to inspect, download, read about, or reproduce the dataset.

Download

Not reported

Code

Not reported

Supplementary

Not reported

Quick facts

Vehicle type
UAV
Environment
Outdoor
Data origin
Simulation
Sensor count
1
Ground truth
Available
Calibration
Not reported
Annotations
Available

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

1 reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Pitot tube

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Not reported Other Not reported Not reported 10 Hz Full Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Unknown
Accuracy notes
The paper calls the drone state exact; no numerical error bound is given.
Reference notes
AirSim-generated per-frame pose, with velocity and acceleration also supplied; no physical reference apparatus is asserted.
Other Other Not reported Not reported 10 Hz Full Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Unknown
Reference notes
Automatically generated depth maps at 1280 x 720; reported range 0–100 m. This is a simulator product, not a depth-camera measurement.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Not reported
Processed parameters
Not reported
Raw calibration data
Not reported
Calibration targets
Not reported

Calibration data

Calibration reproducibility details are not reported.

Notes

Not reported

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Only two simulated environments: a suburban town and a park.

No added collection-stage corruption or sensor noise; severe crashes and stuck flights are filtered, although near misses and minor crashes may remain.

Rain, fog, snow, dusk and bright night imagery create visibility challenges.

Cameras

front_rgb

Model
Not reported
Setup
Mono
Count
1
Effective cameras
1
Modality
Rgb
Resolution
1280 x 720
Rate
10 Hz
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Simulated front-facing camera. Each flight has 100 observations over 10 seconds. Depth, semantic masks, flow and normals are accompanying generated products, not extra physical cameras.

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Pitot tube

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
Not reported
Class count
10
Semantic segmentation Available
Instance segmentation Not reported
Object detection Not reported
Optical flow Available
Depth ground truth Available

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
kolbeinsson2024ddos

BibTeX

Not reported

Dataset corrections

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