| --- |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| language: |
| - en |
| pretty_name: Songdo Vision |
| size_categories: |
| - 1K<n<10K |
| annotations_creators: |
| - expert-generated |
| source_datasets: |
| - original |
| viewer: false |
| tags: |
| - image |
| - object-detection |
| - aerial-imagery |
| - drone |
| - birds-eye-view |
| - vehicle-detection |
| - traffic-monitoring |
| - smart-city |
| - urban-traffic |
| - bounding-box |
| - COCO |
| - YOLO |
| - pascal-voc |
| - songdo |
| - geo-trax |
| - arxiv:2411.02136 |
| --- |
| |
| # Songdo Vision: Vehicle Annotations from High-Altitude BEV Drone Imagery in a Smart City |
|
|
| [](https://doi.org/10.5281/zenodo.13828407) |
| [](https://github.com/rfonod/geo-trax) |
| [](LICENSE) |
| [](https://doi.org/10.1016/j.trc.2025.105205) |
| [](https://arxiv.org/abs/2411.02136) |
| [](https://huggingface.co/rfonod/geo-trax) |
| [](https://huggingface.co/spaces/rfonod/geo-trax) |
| [](https://huggingface.co/datasets/rfonod/songdo-traffic) |
| [](https://www.real-lab.ch/geo-trax) |
|
|
| **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](https://github.com/rfonod/geo-trax) pipeline and the associated |
| [publication](https://doi.org/10.1016/j.trc.2025.105205). |
|
|
| > **📦 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`](https://doi.org/10.5281/zenodo.13828407). See |
| > [Access the dataset](#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](https://creativecommons.org/licenses/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](https://zenodo.org/records/13828408)** · |
| DOI [`10.5281/zenodo.13828407`](https://doi.org/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`](https://github.com/dvolgyes/zenodo_get):** |
|
|
| ```bash |
| pip install zenodo_get |
| zenodo_get 10.5281/zenodo.13828407 # fetches all files for the latest version |
| ``` |
|
|
| **Or directly with `requests`:** |
|
|
| ```python |
| 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](https://github.com/rfonod/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`](https://huggingface.co/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](https://huggingface.co/rfonod/geo-trax) for details). |
|
|
| ## Related datasets and resources |
|
|
| - **Songdo Traffic**: companion georeferenced trajectory dataset: |
| [`10.5281/zenodo.13828384`](https://doi.org/10.5281/zenodo.13828384) · |
| HF [`rfonod/songdo-traffic`](https://huggingface.co/datasets/rfonod/songdo-traffic) |
| - **Geo-trax detector**: HF model [`rfonod/geo-trax`](https://huggingface.co/rfonod/geo-trax) |
| - **Live demo**: interactive 🤗 Space — [`rfonod/geo-trax` (Spaces)](https://huggingface.co/spaces/rfonod/geo-trax) |
| - **Source video recordings** (not open access): |
| [`10.5075/EPFL.20.500.14299/253923`](https://doi.org/10.5075/EPFL.20.500.14299/253923) |
| - **Publication**: *Transportation Research Part C* (2025): |
| [`10.1016/j.trc.2025.105205`](https://doi.org/10.1016/j.trc.2025.105205) · |
| [arXiv:2411.02136](https://arxiv.org/abs/2411.02136) |
| - **Software**: Geo-trax: [github.com/rfonod/geo-trax](https://github.com/rfonod/geo-trax) · |
| [demo video](https://youtu.be/gOGivL9FFLk) |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the associated publication: |
|
|
| ```bibtex |
| @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: |
|
|
| ```bibtex |
| @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)](https://creativecommons.org/licenses/by/4.0/) |
| license; see the [LICENSE](LICENSE) file for the full terms. You are free to share and adapt the |
| material for any purpose, provided you give appropriate credit. |
|
|
