Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City
The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo International Business District, South Korea, this dataset consists of 5,419 annotated video frames, featuring approximately 300,000 vehicle instances categorized into four classes: - Car (including vans and light-duty vehicles) - Bus - Truck - Motorcycle
This dataset can serve as a benchmark for aerial vehicle detection, supporting research and real-world applications in intelligent transportation systems, traffic monitoring, and aerial vision-based mobility analytics. It was developed in the context of a multi-drone experiment aimed at enhancing geo-referenced vehicle trajectory extraction.
f7f0f4b8-53b5-492f-80e3-a388078ce1cc
EPFL
Korea Advanced Institute of Science and Technology
Korea Advanced Institute of Science and Technology
2025-03-17
1
CC BY
| Funder | Funding(s) | Grant NO |
Board of the Swiss Federal Institutes of Technology | Open Research Data (ORD) Program of the ETH Board | |
Swiss National Science Foundation | NCCR Automation (phase I) | 180545 |
Innosuisse – Swiss Innovation Agency | CityDronics | 101.645 IP-ENG |
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| Relation | Related work | URL/DOI |
IsDescribedBy | Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery | |
IsSupplementedBy | Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City | |
IsVersionOf | Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City | |
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