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README.md

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+---
+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
+
+[![Zenodo](https://img.shields.io/badge/Zenodo-Dataset%20%26%20Data-blue?logo=zenodo)](https://doi.org/10.5281/zenodo.13828407)
+[![Paper](https://img.shields.io/badge/Journal-10.1016%2Fj.trc.2025.105205-blue)](https://doi.org/10.1016/j.trc.2025.105205)
+[![arXiv](https://img.shields.io/badge/arXiv-2411.02136-b31b1b)](https://arxiv.org/abs/2411.02136)
+[![Geo-trax Model](https://img.shields.io/badge/🤗%20Model-rfonod%2Fgeo--trax-yellow)](https://huggingface.co/rfonod/geo-trax)
+[![GitHub](https://img.shields.io/badge/GitHub-geo--trax-blue?logo=github)](https://github.com/rfonod/geo-trax)
+[![Website](https://img.shields.io/badge/REAL%20Lab-Geo--trax-informational)](https://www.real-lab.ch/geo-trax)
+[![License](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey)](LICENSE)
+
+**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)** &nbsp;·&nbsp;
+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)
+- **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)
+
+## 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.