The University of Sheffield
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Data Repository from the Swarm of UAVs Innovate UK Project, Future Flights Strand 3, UAV Flights Dataset

This repository contains flight data captured by a UAV (Unmanned Aerial Vehicle) during landing approaches in both real-world and simulated environments. The data comprises video recordings in '*.mp4' format, viewable using media players like 'VLC media player' or 'Windows Media Player'. The footage was acquired using onboard cameras mounted on the UAV during real flights and recorded simulation flights using X-Plane 11.

The recordings showcase UAV operations during the critical phases of landing, emphasizing vision-based navigation techniques for runway detection and trajectory evaluation. The dataset captures various scenarios encountered during real and simulated landing approaches, including diverse environmental conditions and flight parameters.

The UAV flight data is instrumental for research and development activities focused on vision-based navigation systems for autonomous aircraft operations. Specifically, the dataset is intended to support tasks such as object detection, segmentation, and decision-making algorithms tailored for UAV landing approach and runway localization.

Researchers and developers can leverage this dataset for reproducible experiments and algorithm validation, aiding in the advancement of technologies related to autonomous landing systems and UAV safety protocols. Importantly, the dataset is shared with explicit consent from all relevant stakeholders involved in the UAV operations depicted in the recordings.

The Ground Truth folder contains the ground truth labels in csv foormat used for the side-lines evaluation of the vision-navigation system. Real and Simulation videos were used for the evaluation, describing the two side-lines of the runway. The videos used as ground truth are included in the real and simulation video repositories. The name of the video can be found in the first column of each .csv file.

Please cite the related article when using the data repository.

Tsapparellas, K., Jelev, N., Waters, J., Brunswicker, S., & Mihaylova, L.S., Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy. 2023 In Proceedings of the IEEE International Conference on Mechatronics and Automation, ICMA, pp. 239-246, 2023.





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