rfonod/songdo-vision

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

Songdo Vision is a high-resolution aerial vehicle-detection dataset of 4K (3840 × 2160) bird's-eye view (BEV) RGB images with axis-aligned bounding-box annotations. It comprises 5,419 annotated drone frames containing ~272k vehicle instances across four classes (car, bus, truck, motorcycle), captured during a large-scale multi-drone urban traffic monitoring experiment over the Songdo International Business District, South Korea. It is the detection dataset behind the Geo-trax pipeline and the associated publication.

📦 The data is hosted on Zenodo, not on Hugging Face. This page is a documentation mirror and index card. The full dataset (images + annotations, ~16.1 GB) lives immutably on Zenodo under DOI 10.5281/zenodo.13828407. See Access the dataset below for how to download it.

Dataset at a glance

Property	Value
Modality	4K (3840 × 2160) RGB images, bird's-eye view (BEV)
Frames	5,419 (4,335 train / 1,084 test; 80/20 split)
Instances	~272,435 (217,311 train / 55,124 test)
Classes	4: 0 car (incl. vans), 1 bus, 2 truck, 3 motorcycle
Annotation type	Axis-aligned (horizontal) bounding boxes
Formats	COCO JSON, YOLO TXT (normalized), Pascal VOC XML
Acquisition	Fleet of 10 drones over 20 intersections (primarily) and some roads in between, 29.97 FPS, 140–150 m altitude, DJI Mavic 3
Collection dates	October 4–7, 2022
Location	Songdo International Business District, South Korea
Total size	~16.1 GB (train.zip + test.zip)
License	CC BY 4.0

Class distribution

ID	Class	Instances
0	Car (incl. vans)	245,047
1	Bus	8,789
2	Truck	14,831
3	Motorcycle	3,768
	Total	272,435

Access the dataset

The dataset is published and versioned on Zenodo. Please download it from the canonical source; it is not redistributed on Hugging Face:

➡️ zenodo.org/records/13828408  ·  DOI 10.5281/zenodo.13828407 (always resolves to the latest version; the current release is v1, record 13828408)

Files on Zenodo:

File	Size	Contents
train.zip	12.91 GB	Training images + annotations (COCO / YOLO / Pascal VOC)
test.zip	3.22 GB	Test images + annotations (COCO / YOLO / Pascal VOC)
names.txt	small	Class names
data.yaml	small	Example YOLO dataset configuration
README.md	small	Dataset documentation
LICENSE.txt	small	CC BY 4.0 terms

Download with zenodo_get:

pip install zenodo_get
zenodo_get 10.5281/zenodo.13828407      # fetches all files for the latest version

Or directly with requests:

import requests

# Resolve the record's files via the Zenodo API, then download each one.
record = requests.get("https://zenodo.org/api/records/13828408").json()
for f in record["files"]:
    url = f["links"]["self"]
    print("Downloading", f["key"], f"({f['size'] / 1e9:.2f} GB)")
    with requests.get(url, stream=True) as r, open(f["key"], "wb") as out:
        for chunk in r.iter_content(chunk_size=1 << 20):
            out.write(chunk)

Relationship to Geo-trax

Songdo Vision is the detection dataset used to train and validate the default vehicle detector in Geo-trax, a pipeline that extracts georeferenced vehicle trajectories from high-altitude drone footage. The released YOLOv8s detector is published separately as the Hugging Face model rfonod/geo-trax and reports its detection metrics on the Songdo Vision test split.

Note: Songdo Vision annotates only the four vehicle classes above. The Geo-trax model was additionally trained on pedestrian and bicycle instances drawn from other sources, but those classes are not annotated here and are not recommended for use (see the model card for details).

Related datasets and resources

• Songdo Traffic: companion georeferenced trajectory dataset: 10.5281/zenodo.13828384 · HF rfonod/songdo-traffic
• Geo-trax detector: HF model rfonod/geo-trax
• Source video recordings (not open access): 10.5075/EPFL.20.500.14299/253923
• Publication: Transportation Research Part C (2025): 10.1016/j.trc.2025.105205 · arXiv:2411.02136
• Software: Geo-trax: github.com/rfonod/geo-trax · demo video

Citation

If you use this dataset, please cite the associated publication:

@article{fonod2025advanced,
  title   = {Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery},
  author  = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume  = {178},
  pages   = {105205},
  year    = {2025},
  doi     = {10.1016/j.trc.2025.105205}
}

Please also cite the dataset itself via its Zenodo record:

@dataset{fonod2025songdovision,
  author    = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas},
  title     = {Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City},
  year      = {2025},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.13828407},
  url       = {https://doi.org/10.5281/zenodo.13828407}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license; see the LICENSE file for the full terms. You are free to share and adapt the material for any purpose, provided you give appropriate credit.