Abstract
Maintaining the normal flight of drones is crucial for drone operators. Analyzing the operation status of drones and adjusting flight parameters are essential to achieve this goal. However, as drone technology continues to evolve, the volume and complexity of spatio-temporal data related to drone flight status have grown exponentially. The complexity of this data poses a challenge to effective visualization, which can impact operators’ analysis and decision-making. Currently, there is limited research on identifying flight attributes from a large collection of drone time series data. Two challenges were identified: (1) visual clutter from spatio-temporal data; (2) effective integration of time and space properties. By collaborating with domain experts, we addressed two challenges with DSTVis, a novel interactive system for operators to visually analyze spatio-temporal data of drones. For Challenge 1, we designed dynamic interactive views by abstracting and stratifying spatio-temporal data, enabling effective exploration of large amounts of data. For Challenge 2, a two-dimensional map is utilized to integrate time information and assist users in comprehending the spatio-temporal properties. The effectiveness of the system is evaluated with a usage scenario on a real-world historical dataset and received positive feedback from experts.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 201904d07020010, the Key Scientific Research Foundation of the Education Department of the Anhui Province under Grant KJ2020A0470, the open Foundation of the Key lab (center) of Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving under Grant IBES2021KF05, and the Scientific and Technological Achievement Cultivation Project of Intelligent Manufacturing Institute of HFUT under Grant IMIPY2021022.
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Chen, F., Yu, Y., Ni, L. et al. DSTVis: toward better interactive visual analysis of Drones’ spatio-temporal data. J Vis 27, 623–638 (2024). https://doi.org/10.1007/s12650-024-00982-2
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DOI: https://doi.org/10.1007/s12650-024-00982-2