Articles | Volume 13, issue 2
https://doi.org/10.5194/essd-13-357-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-357-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
11 Feb 2021
Data description article |
| 11 Feb 2021
| 11 Feb 2021
Crowdsourced air traffic data from the OpenSky Network 2019–2020
Martin Strohmeier
CORRESPONDING AUTHOR
Armasuisse Science and Technology, 3603 Thun, Switzerland
Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
OpenSky Network, 3400 Burgdorf, Switzerland
Xavier Olive
ONERA DTIS, Université de Toulouse, 31055 Toulouse CEDEX 4, France
OpenSky Network, 3400 Burgdorf, Switzerland
Jannis Lübbe
OpenSky Network, 3400 Burgdorf, Switzerland
Matthias Schäfer
OpenSky Network, 3400 Burgdorf, Switzerland
University of Kaiserslautern, 67663 Kaiserslautern, Germany
Vincent Lenders
Armasuisse Science and Technology, 3603 Thun, Switzerland
OpenSky Network, 3400 Burgdorf, Switzerland
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Total article views: 11,832 (including HTML, PDF, and XML)
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Total article views: 2,838 (including HTML, PDF, and XML)
Thereof 2,514 with geography defined
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45 citations as recorded by crossref.
- The ELK global emission inventory for the transport sectors M. Righi et al. https://doi.org/10.5194/essd-18-1619-2026
- Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling T. Doumbia et al. https://doi.org/10.5194/essd-13-4191-2021
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- Soundscape Perception Preference in an Urban Forest Park: Evidence from Moon Island Forest Park in Lu’an City Y. Guo et al. https://doi.org/10.3390/su142316132
- Global Civil Aviation Emissions Estimates for 2017–2020 Using ADS-B Data F. Quadros et al. https://doi.org/10.2514/1.C036763
- ADS-B Flight Trajectory Tensor Data Recovery Method Based on Truncated Schatten p-Norm W. Zhang et al. https://doi.org/10.3390/app16073217
- Models of similarity in complex networks S. Shvydun https://doi.org/10.7717/peerj-cs.1371
- Quantitative Assessment of Urban Air Collision Risks J. Chang et al. https://doi.org/10.2514/1.D0420
- Analyzing world city network by graph convolutional networks L. Tian et al. https://doi.org/10.1038/s41598-024-69494-1
- A Survey of Link Prediction in Temporal Networks J. Xiong et al. https://doi.org/10.1007/s42979-025-04639-1
- Air traffic modernization and control: ADS-B system implementation update 2022: A review D. Kožović et al. https://doi.org/10.5937/fme2301117K
- Worldwide aviation connections with Antarctica: examining global linkages and their roles in scientific expeditions Y. Jin et al. https://doi.org/10.1080/17538947.2025.2534500
- PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data F. Orchard et al. https://doi.org/10.3389/fpubh.2024.1295117
- A Study on Real-time Threat Analysis and Decision Support System for the Korean Peninsula Based on Multi-AI Model Fusion S. Oh et al. https://doi.org/10.31818/JKNST.2025.9.8.3.458
- Climate Impact Robust Design Optimization for Turbofan Aircraft Engine M. Wiegand et al. https://doi.org/10.1115/1.4072468
- High diversity in Delta variant across countries revealed by genome‐wide analysis of SARS‐CoV‐2 beyond the Spike protein R. Suratekar et al. https://doi.org/10.15252/msb.202110673
- The GraphTempo Framework for Exploring the Evolution of a Graph Through Pattern Aggregation E. Tsoukanara et al. https://doi.org/10.1109/TKDE.2024.3410647
- Aircraft Categorization Approach Using Machine Learning to Analyze Aircraft Behavior N. Vincent-Boulay & C. Marsden https://doi.org/10.2514/1.D0398
- Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model J. Wang et al. https://doi.org/10.3390/atmos15101251
- SPDTM: A shapley-value based preference discovery and task matching to maximize satisfaction in mobile crowd sensing Z. Huang et al. https://doi.org/10.1016/j.comnet.2025.111602
- Framework for assessing and easing global COVID-19 travel restrictions T. Le et al. https://doi.org/10.1038/s41598-022-10678-y
- An assessment of aerial firefighting response times between agencies during the 2020 fire season in California S. Magstadt et al. https://doi.org/10.1016/j.ijdrr.2026.106105
- Inferences for random graphs evolved by clustering attachment N. Markovich et al. https://doi.org/10.1016/j.jspi.2025.106332
- Implementation of a Long-Range Air Traffic Flow Management for the Asia-Pacific Region M. Schultz et al. https://doi.org/10.1109/ACCESS.2021.3110371
- A Temporal Graph Dataset of Bitcoin Entity-Entity Transactions C. Coquidé & R. Cazabet https://doi.org/10.1038/s41597-025-04595-8
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- Visual analysis of changes in European air transport during the COVID-19 pandemic from interactive maps Y. Abilpatta & V. Voženílek https://doi.org/10.2478/mgrsd-2023-0038
- Study of a representative wind selection method using track data to evaluate Pacific flight operations H. Hirabayashi et al. https://doi.org/10.1016/j.jairtraman.2024.102639
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Saved (final revised paper)
Latest update: 06 Oct 2026
Short summary
Flight data have been used widely for research by academic researchers and (supra)national institutions. Example domains range from epidemiology (e.g. examining the spread of COVID-19 via air travel) to economics (e.g. use as proxy for immediate forecasting of the state of a country's economy) and Earth sciences (climatology in particular). Until now, accurate flight data have been available only in small pieces from closed, proprietary sources. This work changes this with a crowdsourced effort.
Flight data have been used widely for research by academic researchers and (supra)national...
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