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  4. Underlying Cut Drone Video Data for Songdo Traffic and Songdo Vision Datasets
 
recorded data

Underlying Cut Drone Video Data for Songdo Traffic and Songdo Vision Datasets

Fonod, Robert  orcid-logo
•
Cho, Haechan
•
Yeo, Hwasoo
Show more
2025
EPFL

This dataset contains the original drone video segments captured during the collaborative multi-drone experiment conducted by KAIST and EPFL in the Songdo International Business District, South Korea, from October 4–7, 2022. These video segments represent the pre-processed, stable hovering footage used to generate the publicly available Songdo Traffic (DOI:10.5281/zenodo.13828384) and Songdo Vision (DOI:10.5281/zenodo.13828408) datasets.

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Type
recorded data
DOI
10.5075/EPFL.20.500.14299/253923
ACOUA ID

fd8c9753-5dd7-4830-9be8-53d42deed12e

Author(s)
Fonod, Robert  orcid-logo

EPFL

Cho, Haechan
Yeo, Hwasoo
Geroliminis, Nikolaos  

EPFL

Date Issued

2025

Publisher

EPFL

License

CC BY

Subjects

Information Technology

Note

Request Access

Please be aware that accessing the preserved dataset may take up to 2 work days due to our long-term solution design. We appreciate your understanding.

EPFL units
LUTS  
RelationRelated workURL/DOI

IsSourceOf

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

https://infoscience.epfl.ch/handle/20.500.14299/248163

IsSourceOf

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

https://infoscience.epfl.ch/handle/20.500.14299/248162

IsSupplementTo

Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery

https://infoscience.epfl.ch/handle/20.500.14299/248159
Available on Infoscience
September 10, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/253923
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