Overview

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird’s-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectories, this resource represents one of the most extensive aerial traffic datasets publicly available, distinguishing itself through exceptional temporal resolution that captures vehicle movements at 29.97 points per second, enabling unprecedented granularity for advanced urban mobility analysis.

📌 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.

🔗 Companion dataset: For high-resolution annotated images with vehicle bounding boxes supporting aerial detection research, see Songdo Vision: 10.5281/zenodo.13828407.

Dataset Composition

The dataset consists of five primary components:

• Trajectory Data: 80 ZIP archives containing high-resolution vehicle trajectories with georeferenced positions, speeds and acceleration profiles, and other metadata.
• Orthophoto Cut-Outs: High-resolution (8000×8000 pixel) orthophoto images for each monitored intersection, together with the georeferencing parameters needed to convert orthophoto pixels into real-world coordinates.
• Road and Lane Segmentations: CSV files defining lane polygons within road sections, facilitating mapping of vehicle positions to road segments and lanes.
• Master Frames: Reference images and transformation parameters relating stabilized video frames to the orthophoto cut-out coordinate system, for users who wish to reconstruct or audit the georeferencing.
• Sample Videos: A selection of 4K UHD drone video samples capturing intersection footage during the experiment.

Data Collection

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