DOI: 10.5281/zenodo.13828407
title: Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

description: Overview

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo International Business District, South Korea, this dataset consists of 5,419 annotated video frames, featuring approximately 300,000 vehicle instances categorized into four classes:



Car (including vans and light-duty vehicles)

Bus

Truck

Motorcycle


This dataset can serve as a benchmark for aerial vehicle detection, supporting research and real-world applications in intelligent transportation systems, traffic monitoring, and aerial vision-based mobility analytics. It was developed in the context of a multi-drone experiment aimed at enhancing geo-referenced vehicle trajectory extraction.

📌 Citation: If you use this dataset in your work, kindly acknowledge it by citing the following article:



Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, Transportation Research Part C: Emerging Technologies, vol. 178, 105205. DOI: 10.1016/j.trc.2025.105205.


🔗 Related dataset: For precisely georeferenced vehicle trajectories extracted from the same large-scale multi-drone experiment, see Songdo Traffic: 10.5281/zenodo.13828384.

Motivation

Publicly available datasets for aerial vehicle detection often exhibit limitations such as:



Non-BeV perspectives with varying angles and distortions

Inconsistent annotation quality, with loose or missing bounding boxes

Lower-resolution imagery, reducing detection accuracy, particularly for smaller vehicles

Lack of annotation detail, especially for motorcycles in dense urban scenes with complex backgrounds


To address these challenges, Songdo Vision provides high-quality human-annotated bounding boxes, with machine learning assistance used to enhance efficiency and consistency. This ensures accurate and reliable ground truth for training and evaluating detection models.

Dataset Composition

The dataset is randomly split into training (80%) and test (20%) subsets:




Subset
Images
Car
Bus
Truck
Motorcycle
Total Vehicles


Train
4,335
195,539
7,030
11,779
2,963
217,311


Test
1,084
49,508
1,759
3,052
805
55,124




 

A subset of 5,274 frames was randomly sampled from drone video sequences, while an additional 145 frames were carefully selected to represent challenging cases, such as motorcycles at pedestrian crossings, in bicycle lanes, near traffic light poles, and around other distinctive road markers where they may blend into the urban environment.

Data Collection

The dataset was collected as part of a collaborative multi-drone experiment conducted by KAIST and EPFL in Songdo, South Korea, from October 4–7, 2022.



A fleet of 10 drones monitored 20 busy intersections, executing advanced flight plans to optimize coverage.

4K (3840×2160) RGB video footage was recorded at 29.97 FPS from altitudes of 140–150 meters.

Each drone flew 10 sessions per day, covering peak morning and afternoon periods.

The experiment resulted in 12TB of 4K raw video data.


More details on the experimental setup and data processing pipeline are available in [1].

Bounding Box Annotations & Formats

Annotations were generated using a semi-automated object detection annotation process in Azure ML Studio, leveraging machine learning-assisted bounding box detection with human verification to ensure precision.

Each annotated frame includes categorized, axis-aligned bounding boxes, stored in three widely-used formats:

1. COCO JSON format



Single annotation file per dataset subset (i.e., one for training, one for testing).

Contains metadata such as image dimensions, bounding box coordinates, and class labels.

Example snippet:


{
  "images": [{"id": 1, "file_name": "0001.jpg", "width": 3840, "height": 2160}],
  "annotations": [{"id": 1, "image_id": 1, "category_id": 2, "bbox": [500, 600, 200, 50], "area": 10000, "iscrowd": 0}],
  "categories": [
    {"id": 1, "name": "car"}, {"id": 2, "name": "bus"},
    {"id": 3, "name": "truck"}, {"id": 4, "name": "motorcycle"}
  ]
}

2. YOLO TXT format



One annotation file per image, following the format:


<class_id> <x_center> <y_center> <width> <height>



Bounding box values are normalized to [0,1], with the origin at the top-left corner.

Example snippet:


0 0.52 0.63 0.10 0.05  # Car bounding box
2 0.25 0.40 0.15 0.08  # Truck bounding box

3. Pascal VOC XML format



One annotation file per image, structured in XML.

Contains image properties and absolute pixel coordinates for each bounding box.

Example snippet:


<annotation>
  <filename>0001.jpg</filename>
  <size><width>3840</width><height>2160</height><depth>3</depth></size>
  <object>
    <name>car</name>
    <bndbox><xmin>500</xmin><ymin>600</ymin><xmax>600</xmax><ymax>650</ymax></bndbox>
  </object>
</annotation>

File Structure

The dataset is provided as two compressed archives:

1. Training Data (train.zip, 12.91 GB)

train/
│── coco_annotations.json  # COCO format
│── images/
│   ├── 0001.jpg
│   ├── ...
│── labels/
│   ├── 0001.txt  # YOLO format
│   ├── 0001.xml  # Pascal VOC format
│   ├── ...

2. Testing Data (test.zip, 3.22 GB)

test/
│── coco_annotations.json
│── images/
│   ├── 00027.jpg
│   ├── ...
│── labels/
│   ├── 00027.txt
│   ├── 00027.xml
│   ├── ...

Additional Files



README.md – Dataset documentation (this description)

LICENSE.txt – Creative Commons Attribution 4.0 License

names.txt – Class names (one per line)

data.yaml – Example YOLO configuration file for training/testing


Acknowledgments

In addition to the funding sources listed in the metadata, the creators express their gratitude to Artem Vasilev for his dedicated efforts in data annotation. We also thank the research teams of Prof. Simon Oh (Korea University) and Prof. Minju Park (Hannam University) for their assistance during the data collection campaign, including the provision of drone equipment and student support.

Citation & Attribution

Preferred Citation: If you use Songdo Vision for any purpose, whether academic research, commercial applications, open-source projects, or benchmarking efforts, please cite our accompanying article [1]:



Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, Transportation Research Part C: Emerging Technologies, vol. 178, 105205. DOI: 10.1016/j.trc.2025.105205

BibTeX entry:

@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},  publisher = {Elsevier},  doi = {10.1016/j.trc.2025.105205},  url = {https://doi.org/10.1016/j.trc.2025.105205}
}




Dataset Citation (for archival purposes): Although Zenodo automatically provides a formal citation for this dataset (see below), including citation export in various formats such as BibTeX, we kindly request that you reference the above article as the primary source of this work.




Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City (v1). Zenodo. DOI: 10.5281/zenodo.13828408.