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.13828408. Dataset Composition The dataset consists of four 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, used for georeferencing and visualization. Road and Lane Segmentations: CSV files defining lane polygons within road sections, facilitating mapping of vehicle positions to road segments and lanes. Sample Videos: A selection of 4K UHD drone video samples capturing intersection footage during the experiment. 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]. Data Processing The trajectories were extracted using 🚀 Geo-trax, an advanced deep learning framework designed for high-altitude UAV-based traffic monitoring. This state-of-the-art pipeline integrates vehicle detection, tracking, trajectory stabilization, and georeferencing to extract high-accuracy traffic data from drone footage. 🎥 A demonstration of the Geo-trax framework in operation is available at: https://youtu.be/gOGivL9FFLk Key Processing Steps: Vehicle Detection & Tracking: Vehicles were detected and tracked across frames using a deep learning-based detector and motion-model-based tracking algorithm. Trajectory Stabilization: A novel track stabilization method was applied using detected vehicle bounding boxes as exclusion masks in image registration. Georeferencing & Coordinate Transformation: Each trajectory was transformed into global (WGS84), local Cartesian, and orthophoto coordinate systems. Vehicle Metadata Estimation: In addition to time-stamped vehicle trajectories, various metadata attributes were also extracted, including vehicle dimensions and type, speed, acceleration, class, lane number, road section, and visibility status. More details on the extraction methodology are available in [1]. File Structure & Formats 1. Trajectory Data (Daily Intersection ZIPs, 16.2 MB ~ 360.2 MB) The trajectory data is organized into 80 ZIP files, each containing traffic data for a specific intersection and day of the experiment. File Naming Convention: YYYY-MM-DD_intersectionID.zip YYYY-MM-DD represents the date of data collection (2022-10-04 to 2022-10-07). intersectionID is a unique identifier for one of the 20 intersections where data was collected (A, B, C, E, …, U). The letter D is reserved to denote "Drone". Each ZIP file contains 10 CSV files, each corresponding to an individual flight session: YYYY-MM-DD_intersectionID.zip │── YYYY-MM-DD_intersectionID_AM1.csv ├── … │── YYYY-MM-DD_intersectionID_AM5.csv │── YYYY-MM-DD_intersectionID_PM1.csv ├── … └── YYYY-MM-DD_intersectionID_PM5.csv Here, AM1-AM5 and PM1-PM5 denote morning and afternoon flight sessions, respectively. For example, 2022-10-04_S_AM1.csv contains all extracted trajectories from the first morning session of the first day at the intersection 'S'. CSV File Example Structure: Each CSV file contains high-frequency trajectory data, formatted as follows (d.p. = decimal place): Dataset Column Name Format / Units Data Type Explanation Vehicle_ID 1, 2, … Integer Unique vehicle identifier within each CSV file Local_Time hh:mm:ss.sss String Local Korean time (GMT+9) in ISO 8601 format Drone_ID 1, 2, …, 10 Integer Unique identifier for the drone capturing the data Ortho_X, Ortho_Y px (1 d.p.) Float Vehicle center coordinates in the orthophoto cut-out image Local_X, Local_Y m (2 d.p.) Float KGD2002 / Central Belt 2010 planar coordinates (EPSG:5186) Latitude, Longitude ° DD (7 d.p.) Float WGS84 geographic coordinates in decimal degrees (EPSG:4326) Vehicle_Length*, Vehicle_Width* m (2 d.p.) Float Estimated physical dimensions of the vehicle Vehicle_Class Categorical (0–3) Integer Vehicle type: 0 (car/van), 1 (bus), 2 (truck), 3 (motorcycle) Vehicle_Speed* km/h (1 d.p.) Float Estimated speed computed from trajectory data using Gaussian smoothing Vehicle_Acceleration* m/s² (2 d.p.) Float Estimated acceleration derived from smoothed speed values Road_Section* N_G String Road section identifier (N = node, G = lane group) Lane_Number* 1, 2, … Integer Lane position (1 = leftmost lane in the direction of travel) Visibility 0/1 Boolean 1 = fully visible, 0 = partially visible in the camera frame * These columns may be empty under certain conditions, see [1] for more details. 2. Orthophoto Cut-Outs (orthophotos.zip, 1.8 GB) For each intersection, we provide the high-resolution orthophoto cut-outs that were used for georeferencing. These 8000×8000 pixel PNG images cover specific areas, allowing users to overlay orthophoto trajectories within the road network. orthophotos/ │── A.png │── B.png │── … └── U.png For more details on the orthophoto generation process, refer to [1]. 3. Orthophoto Segmentations (segmentations.zip, 24.9 KB) We provide the road and lane segmentations for each orthophoto cut-out, stored as CSV files where each row defines a lane polygon within a road section. Each section (N_G) groups lanes moving in the same direction, with lanes numbered sequentially from the innermost outward. The CSV files are structured as follows: segmentations/ │── A.csv │── B.csv │── … └── U.csv Each file contains the following columns: Section: Road section ID (N_G format). Lane: Lane number within the section. tlx, tly, blx, bly, brx, bry, trx, try: Polygon corner coordinates. These segmentations enabled trajectory points to be mapped to specific lanes and sections in our trajectory dataset. Vehicles outside segmented areas (e.g., intersection centers) remain unlabeled. Perspective distortions may also cause misalignments for taller vehicles. 4. Sample Videos (sample_videos.zip, 26.8 GB) The dataset includes 29 video samples, each capturing the first 60 seconds of drone hovering over its designated intersection during the final session (PM5) on October 7, 2022. These high-resolution 4K videos provide additional context for trajectory analysis and visualization, complementing the orthophoto cut-outs and segmentations. sample_videos/ │── A_D1_2022-10-07_PM5_60s.mp4 │── A_D2_2022-10-07_PM5_60s.mp4 │── B_D1_2022-10-07_PM5_60s.mp4 │── … └── U_D10_2022-10-07_PM5_60s.mp4 Additional Files README.md – Dataset documentation (this file) LICENSE.txt – Creative Commons Attribution 4.0 License Known Dataset Artifacts and Limitations While this dataset is designed for high accuracy, users should be aware of the following known artifacts and limitations: Trajectory Fragmentation: Trajectories may be fragmented for motorcycles in complex road infrastructure scenarios (pedestrian crossings, bicycle lanes, traffic signals) and for certain underrepresented truck variants. Additional fragmentations occurred when drones experienced technical issues during hovering, necessitating mid-recording splits that naturally resulted in divided trajectories. Vehicle ID Ambiguities: The largest Vehicle_ID in a CSV file does not necessarily indicate the total number of unique vehicles. Kinematic Estimation Limitations: Speed and acceleration values are derived from raw tracking data and may be affected by minor errors due to detection inaccuracies, stabilization artifacts, and applied interpolation and smoothing techniques. Vehicle Dimension Estimation: Estimates may be unreliable for stationary or non-axially moving vehicles and can be affected by bounding box overestimations capturing protruding vehicle parts or shadows. Lane and Section Assignment Inaccuracies: Perspective effects may cause vehicles with significant heights, such as trucks or buses, to be misassigned to incorrect lanes or sections in the orthophoto. Occasional pedestrian pair misclassifications: Rarely, two pedestrians walking side by side may be briefly mistaken for a motorcycle, but such instances are short-lived and typically removed by the short trajectory filter. For a comprehensive discussion of dataset limitations and validation procedures, refer to [1]. Acknowledgments In addition to the funding sources listed in the metadata, the creators express their gratitude to Sohyeong Kim, Yura Tak, and Weijiang Xiong for their invaluable assistance in the data-wrangling process, Artem Vasilev for his dedicated efforts in data annotation, Jasso Espadaler Clapés for his insightful contributions to vehicle dimension estimation, and Muhammad Ahmed for his support in creating the experimental orthophoto for validation purposes and his contributions to road segmentation. We also thank DroMii company for producing the primary orthophoto and Stanford Center at the Incheon Global Campus (SCIGC) for providing the on-board recorded trajectories of their autonomous vehicle. 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 Traffic 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 Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City (v1). Zenodo. DOI: 10.5281/zenodo.13828384.