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 bird's-eye view (BEV) RGB images with axis-aligned bounding-box annotations. It comprises 5,419 annotated drone frames containing ~274k 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 RGB images, bird's-eye view (BEV) — 3840 × 2160, except 8 frames recorded portrait (2160 × 3840)
Frames	5,419 (4,335 train / 1,084 test; 80/20 split)
Instances	274,190 (218,662 train / 55,528 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)	246,581
1	Bus	8,818
2	Truck	14,941
3	Motorcycle	3,850
	Total	274,190

What changed in v2

v2 is an annotation-quality release; the images are byte-identical to v1. 196 invalid boxes were removed (road markings, rooftop plant, construction machinery, duplicates, degenerate boxes) and 1,951 vehicles missed by the original annotation pass were added. All three annotation formats are now generated from a single source and agree exactly, where COCO and Pascal VOC previously disagreed by up to 1 px on ~75% of boxes.

Class	v1	Removed	Added	v2
Car	245,047	140	1,674	246,581
Bus	8,789	10	39	8,818
Truck	14,831	34	144	14,941
Motorcycle	3,768	12	94	3,850
Total	272,435	196	1,951	274,190

Candidates for both removals and additions were ranked with a detector and then confirmed by a human reviewer; nothing was changed automatically. Annotations carried over from v1 keep their original COCO ids (≤ 272435); annotations added in v2 are numbered from 1000001, so the two sets can be told apart — filtering id <= 272435 reproduces the v1-provenance annotation set. See the dataset README on Zenodo for the full changelog and known limitations.

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:

➡️ DOI 10.5281/zenodo.13828407 (concept DOI — always resolves to the latest version, currently v2; each release also has its own version DOI, shown on the record page)

Files on Zenodo:

File	Size	Contents
train.zip	≈12.9 GB	Training images + annotations (COCO / YOLO / Pascal VOC)
test.zip	≈3.2 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

# Follow the concept DOI to whichever record is currently the latest version,
# then download that record's files.
record_id = requests.get(
    "https://doi.org/10.5281/zenodo.13828407", allow_redirects=True
).url.rstrip("/").split("/")[-1]
record = requests.get(f"https://zenodo.org/api/records/{record_id}").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.

The released Geo-trax detector was trained and validated on Songdo Vision v1, and the detection metrics on its model card refer to the v1 test split. It has not been retrained on v2.

This also matters for benchmarking: in v2, candidate missed vehicles were surfaced with a Geo-trax detector before human confirmation, so evaluating that same model family on the v2 test split carries a small favourable bias. It is bounded — 443 boxes, 0.8% of the test annotations — and filtering COCO id <= 272435 restores the v1-provenance test set for a strictly model-independent comparison.

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
• Live demo: interactive 🤗 Space — rfonod/geo-trax (Spaces)
• 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{fonod2026songdovision,
  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      = {2026},
  version   = {v2},
  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.