UAV
AirNav
UAV vision-and-language navigation
Urban aerial navigation benchmark with approximately 137K samples, synthesized trajectories and instructions generated through a human–LLM pipeline with ten personas.
Access links
Primary places to inspect, download, read about, or reproduce the dataset.
Quick facts
- Vehicle type
- UAV
- Environment
- Outdoor
- Data origin
- Not reported
- Sensor count
- 0
- Ground truth
- Not reported
- Calibration
- Not reported
- Annotations
- Available
Sensor overview
High-level sensor availability before the detailed sensor records below.
Ground truth
Reference scope, method, rate, coverage, and provenance.
Not reported
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 reportedKnown or intentional limitations
Reported constraints and characteristics to check before using the dataset.
Geographic, urban-style and infrastructure coverage is constrained by the source datasets.
Fixed viewpoints and altitude distributions underrepresent some altitude variation, occlusion and extreme lighting.
Discrete action modeling approximates continuous flight dynamics and may introduce trajectory errors.
Limited physical evaluation leaves a sim-to-real gap in noise, viewpoints and control.
Cameras
Not reported
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
- Not reported
Semantic segmentation
Not reported
Instance segmentation
Not reported
Object detection
Not reported
Optical flow
Not reported
Depth ground truth
Not reported
Citation
Citation key and BibTeX kept at the end of the page for reference.
@misc{cai2026airnav,
title = {AirNav: A Large-Scale UAV Vision-and-Language Navigation Dataset with Natural and Diverse Instructions},
author = {Hengxing Cai and Yijie Rao and Ligang Huang and Zanyang Zhong and Jinhan Dong and Jingjun Tan and Changhao Nai and Jue Hou and Wenhao Lu and Renxin Zhong},
year = {2026},
eprint = {2601.03707},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2601.03707v2}
}
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