--- license: cc-by-4.0 task_categories: - object-detection language: - en pretty_name: Songdo Vision size_categories: - 1K **📦 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.