--- license: cc-by-4.0 task_categories: - object-detection - other language: - en pretty_name: Songdo Traffic size_categories: - 100K **📦 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_.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.