| --- |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| - other |
| language: |
| - en |
| pretty_name: Songdo Traffic |
| size_categories: |
| - 100K<n<1M |
| annotations_creators: |
| - expert-generated |
| source_datasets: |
| - original |
| viewer: false |
| tags: |
| - trajectory |
| - vehicle-trajectories |
| - georeferenced |
| - aerial-imagery |
| - drone |
| - birds-eye-view |
| - vehicle-detection |
| - traffic-monitoring |
| - smart-city |
| - urban-traffic |
| - multi-drone |
| - WGS84 |
| - EPSG-4326 |
| - songdo |
| - geo-trax |
| - arxiv:2411.02136 |
| --- |
| |
| # Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City |
|
|
| [](https://doi.org/10.5281/zenodo.13828384) |
| [](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-vision) |
| [](https://www.real-lab.ch/geo-trax) |
| [](https://youtu.be/gOGivL9FFLk) |
|
|
| **Songdo Traffic** is a large-scale dataset of **~700,000 georeferenced vehicle trajectories** |
| extracted from high-altitude bird's-eye view (BEV) drone footage. The trajectories carry WGS84 |
| coordinates, kinematics (speed, acceleration), estimated vehicle dimensions, class, and lane/road |
| assignment, sampled at **29.97 points per second**. They were captured during a large-scale |
| multi-drone urban traffic monitoring experiment over the Songdo International Business District, |
| South Korea, and constitute one of the most extensive aerial traffic datasets publicly available. |
| Songdo Traffic is the trajectory dataset produced by 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 (trajectories, |
| > orthophotos, segmentations, master frames, and sample videos; ~70 GB) lives immutably on |
| > Zenodo under concept DOI |
| > [`10.5281/zenodo.13828384`](https://doi.org/10.5281/zenodo.13828384) (always resolves to the |
| > latest version; the current release is **v2**, record |
| > [`17924857`](https://zenodo.org/records/17924857)). See |
| > [Access the dataset](#access-the-dataset) below for how to download it. |
|
|
| ## Dataset at a glance |
|
|
| | Property | Value | |
| |---|---| |
| | Modality | Georeferenced vehicle trajectories extracted from 4K (3840 × 2160) RGB BEV video | |
| | Trajectories | ~700,000 unique trajectories | |
| | Temporal resolution | 29.97 points/second (per the 29.97 FPS source video) | |
| | Classes | 4: `0` car (incl. vans), `1` bus, `2` truck, `3` motorcycle | |
| | Coordinate systems | Orthophoto pixels · local Cartesian (KGD2002 / Central Belt 2010, EPSG:5186) · WGS84 (EPSG:4326) | |
| | Per-vehicle attributes | Position, speed, acceleration, estimated length/width, class, lane, road section, visibility | |
| | Acquisition | Fleet of 10 drones over 20 intersections (A–U), 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 | ~70 GB (trajectories ~41 GB + orthophotos, master frames, segmentations, sample videos) | |
| | License | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
|
|
| ### Trajectory CSV schema |
|
|
| Each trajectory CSV contains one row per vehicle observation with the following fields: |
|
|
| | Column | Format | Type | Description | |
| |---|---|---|---| |
| | `Vehicle_ID` | 1, 2, … | int | Unique identifier within the CSV | |
| | `Local_Time` | hh:mm:ss.sss | str | Korean local time (GMT+9), ISO 8601 | |
| | `Drone_ID` | 1–10 | int | Source drone identifier | |
| | `Ortho_X`, `Ortho_Y` | pixels (1 d.p.) | float | Orthophoto image coordinates | |
| | `Local_X`, `Local_Y` | meters (2 d.p.) | float | KGD2002 / Central Belt 2010 (EPSG:5186) | |
| | `Latitude`, `Longitude` | DD (7 d.p.) | float | WGS84 geographic (EPSG:4326) | |
| | `Vehicle_Length`, `Vehicle_Width` | meters (2 d.p.) | float | Estimated physical dimensions | |
| | `Vehicle_Class` | 0–3 | int | 0 = car/van, 1 = bus, 2 = truck, 3 = motorcycle | |
| | `Vehicle_Speed` | km/h (1 d.p.) | float | Computed with Gaussian smoothing | |
| | `Vehicle_Acceleration` | m/s² (2 d.p.) | float | Derived from smoothed speed | |
| | `Road_Section` | N_G | str | Node and lane-group identifier | |
| | `Lane_Number` | 1, 2, … | int | Lane position (1 = leftmost) | |
| | `Visibility` | 0/1 | bool | Fully (1) / partially (0) visible in frame | |
|
|
| ## 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/17924857](https://zenodo.org/records/17924857)** · |
| DOI [`10.5281/zenodo.13828384`](https://doi.org/10.5281/zenodo.13828384) *(always resolves to the |
| latest version; the current release is v2, record `17924857`)* |
|
|
| **Files on Zenodo:** |
|
|
| | File(s) | Size | Contents | |
| |---|---|---| |
| | `YYYY-MM-DD_<ID>.zip` (80 archives) | 16.2–360.2 MB each | Trajectory CSVs per intersection (A–U) and day; 10 sessions each (AM1–AM5, PM1–PM5) | |
| | `orthophotos.zip` | 1.8 GB | 8000 × 8000 px orthophoto cut-outs per intersection + georeferencing parameters | |
| | `master_frames.zip` | 248.6 MB | Reference frames + homography parameters for georeferencing reproducibility | |
| | `segmentations.zip` | 24.9 KB | Road/lane polygon definitions (CSV) per intersection | |
| | `sample_videos.zip` | 26.8 GB | 29 sample 4K UHD drone clips (first 60 s of the PM5 session, Oct 7, 2022) | |
| | `README.md` | small | Dataset documentation | |
| | `LICENSE.txt` | small | CC BY 4.0 terms | |
|
|
| > **v2 update:** version 2 adds the master frames and georeferencing parameters (for full |
| > reproducibility of the coordinate transforms) and updated coordinate-transformation documentation. |
|
|
| **Download with [`zenodo_get`](https://github.com/dvolgyes/zenodo_get):** |
|
|
| ```bash |
| pip install zenodo_get |
| zenodo_get 10.5281/zenodo.13828384 # 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/17924857").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 Traffic is the trajectory dataset produced by |
| **[Geo-trax](https://github.com/rfonod/geo-trax)**, a pipeline that extracts georeferenced vehicle |
| trajectories from high-altitude drone footage. The trajectories are generated end-to-end by |
| detecting and tracking vehicles, stabilizing the footage, georeferencing against orthophotos, and |
| estimating per-vehicle kinematics, dimensions, and lane assignments. The default YOLOv8s detector |
| driving the pipeline is published separately as the Hugging Face model |
| [`rfonod/geo-trax`](https://huggingface.co/rfonod/geo-trax), trained and validated on the companion |
| detection dataset [`rfonod/songdo-vision`](https://huggingface.co/datasets/rfonod/songdo-vision). |
|
|
| > **Note:** These trajectories are extracted automatically and carry estimation artifacts |
| > (fragmentation, kinematic/dimension estimation errors, occasional lane misassignment). See |
| > [Known limitations](#known-limitations) below and the |
| > [model card](https://huggingface.co/rfonod/geo-trax) before using them. |
|
|
| ## Known limitations |
|
|
| The trajectories are extracted automatically and carry estimation artifacts. In brief: |
|
|
| - **Trajectory fragmentation:** motorcycles fragment in complex infrastructure; some trucks are |
| underrepresented; drone technical issues occasionally split recordings mid-session. |
| - **Vehicle ID ambiguities:** the largest `Vehicle_ID` in a CSV does not equal the total count of |
| unique vehicles. |
| - **Kinematics:** speed/acceleration are affected by detection inaccuracies, stabilization |
| artifacts, interpolation, and smoothing. |
| - **Dimension estimation:** unreliable for stationary or non-axially-aligned vehicles. |
| - **Lane/section assignment:** perspective effects can misassign tall vehicles (trucks/buses) to |
| adjacent lanes. |
|
|
| See the Zenodo record and the [publication](https://doi.org/10.1016/j.trc.2025.105205) for the |
| full discussion of limitations. |
|
|
| ## Related datasets and resources |
|
|
| - **Songdo Vision**: companion vehicle-detection (annotated image) dataset: |
| [`10.5281/zenodo.13828407`](https://doi.org/10.5281/zenodo.13828407) · |
| HF [`rfonod/songdo-vision`](https://huggingface.co/datasets/rfonod/songdo-vision) |
| - **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{fonod2025songdotraffic, |
| author = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas}, |
| title = {Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City}, |
| year = {2025}, |
| publisher = {Zenodo}, |
| doi = {10.5281/zenodo.13828384}, |
| url = {https://doi.org/10.5281/zenodo.13828384} |
| } |
| ``` |
|
|
| ## 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. |
|
|
