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  4. Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City
 
dataset

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

Fonod, Robert  orcid-logo
•
Cho, Haechan
•
Yeo, Hwasoo
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March 17, 2025
Zenodo

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.

  • Details
  • Metrics
Type
dataset
DOI
10.5281/zenodo.13828408
ACOUA ID

f7f0f4b8-53b5-492f-80e3-a388078ce1cc

Author(s)
Fonod, Robert  orcid-logo

EPFL

Cho, Haechan

Korea Advanced Institute of Science and Technology

Yeo, Hwasoo

Korea Advanced Institute of Science and Technology

Geroliminis, Nikolaos  

EPFL

Date Issued

2025-03-17

Version

1

Publisher

Zenodo

License

CC BY

Subjects

Computer vision

•

Aerial Vehicle Detection

•

Drone Imagery

•

Bird's-Eye View (BeV)

•

Traffic Monitoring

•

Smart City Analytics

•

Deep learning

•

Object Detection Dataset

•

Bounding Box Annotations

•

High-Altitude UAV

•

Vehicle Detection

•

COCO Dataset Format

•

YOLO Annotations

•

Pascal VOC Format

•

Urban Traffic Analysis

•

Multi-Class Object Detection

•

Machine Learning Dataset

Additional link
EPFL units
LUTS  
FunderFunding(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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RelationRelated workURL/DOI

IsDescribedBy

Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery

https://infoscience.epfl.ch/handle/20.500.14299/248159

IsSupplementedBy

Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City

https://doi.org/10.5281/zenodo.13828384

IsVersionOf

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

https://doi.org/10.5281/zenodo.13828407
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Available on Infoscience
March 21, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/248163
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