Data Tools provided by the OpenSky Community

 

On this page, we will collect and briefly introduce some of the OpenSky-related tools provided by our extraordinary community, covering different functionalities in the main data science languages Python, R and Matlab. Principally, these are tools to access and process data either via our Impala shell (acess request required), our live API or our prepared datasets.

Naturally, the OpenSky Network Association does not maintain these tools and thus cannot give any support. For further questions on any of these, you should ask the respective maintainer or try our forum. Pretty much all of these tools are developed as open source, so you can help improve them and report any bugs. This list is work in progress, if you want to add your OpenSky tool or know of one that should be on this page, please write us atThis email address is being protected from spambots. You need JavaScript enabled to view it.

 

Further reading suggestions for better understanding of the air traffic data we use include:

 

1. traffic – Air traffic data processing in Python

Developer:

Xavier Olive, Onera

 

Description:

The traffic library helps working with common sources of air traffic data. It provides a Python interface for our Impala shell among many other functions.

Its main purpose is to provide data analysis methods commonly applied to trajectories and airspaces. When a specific function is not provided, the access to the underlying structure is direct, through an attribute pointing to a pandas dataframe.

The library also offers facilities to parse and/or access traffic data from open sources of ADS-B traffic like the OpenSky Network or Eurocontrol DDR files. It is designed to be easily extendable to other sources of data.

 

Source:

https://github.com/xoolive/traffic

 

Scientific Paper:

Xavier Olive and Luis Basora.
A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering.
In Proceedings of the 7th OpenSky Workshop 2019.
November 2019, 73–84.

 

2. pyModeS + pyOpenSky

Developer:

Junzi Sun, TU Delft

 

Description:

pyModeS is a Python library designed to decode Mode-S (including ADS-B) message. It can be imported to your python project or used as a standalone tool to view and save live traffic data. pyOpenSky is a compatible interface for our Impala database, providing the options to:

  1. Query raw and ADS-B messages from OpenSky Impala database.
  2. Decode OpenSky Comm-B information automatically using pyModeS.

The pyopensky connects the pyModeS decoder and OpenSky-network raw Mode-S data. It aims at making the Enhance Mode-S information form OpenSky network more accessible for researchers.

It can automatically retrieve and download data in rollcall_replies_data4 table from the OpenSky Impala database, and then decodes several common Mode-S Comm-B message types.

 

Sources:

https://github.com/junzis/pyopensky
https://github.com/junzis/pyModeS

 

Scientific Papers:

Junzi Sun and Jacco Hoekstra.
Integrating pyModeS and OpenSky Historical Database.
In Proceedings of the 7th OpenSky Workshop 2019.
November 2019, 63–72. 

 

3. em-download-opensky + em-processing-opensky

Developer:

MIT Lincoln Labs

 

Description:

The em-download-opensky repository is a collection of shell scripts and MATLAB code to query the OpenSky Network Impala database and download crowdsourced observations of aircraft.

The download data can then processed using the em-processing-opensky repository. Additionally, this software is a MATLAB and shell alternative to the Python-based Xavier Olive's traffic and Junzi Sun's pyModeS repositories. The shell scripts can be used independent of the MATLAB software. The software also enables queries based on AGL altitude, aerodromes, airspace class, and time zone. Although this is not a one-to-one MATLAB alternative to the Python repositories, as each repository queries the Impala database slightly different. For example, Olive's traffic software can query the Impala historical database based on an optional geographic footprint of an airspace or user defined shape with MSL altitude; whereas em-download-opensky generates custom bounding boxes based on AGL altitude. Some of the functionality might be useful for US-focused users.

 

 

Sources:

https://github.com/Airspace-Encounter-Models/em-download-opensky
https://github.com/Airspace-Encounter-Models/em-processing-opensky

 

4. R-based Wrappers: openSkies, osn, openskyr

Developers:

Rafael Ayala, Imperial College London (openSkies)

Enrico Spinelli, Eurocontrol (osn)

Luis Gasco, Universidad Politécnica de Madrid (openSkyR)

 

Description:

There are several R-based wrappers available, from different developers providing access to either our live API or the Impala shell. openSkies is the most currently maintained and released on CRAN. It provides a set of tools to retrieve, analyze and plot flight information data in R. It includes a client interface to the OpenSky API.

 

Sources:

https://github.com/Rafael-Ayala/openSkies
https://github.com/espinielli/osn
https://github.com/luisgasco/openskyr

 

5. ADSbDataParser

Developers:

L.L. Schmidt and C. Bloemer-Zurborg, Institute of Flight Guidance, TU Braunschweig

 

Description:

The ADSbDataParser can be used to filter and preprocess ADS-B data and is especially adapted to historic traffic data with format state_vectors_data4 of The OpenSky Network. The data parser is developed using Java technology and m-files are provided to use the parser within MATLAB.

 

Sources:

https://github.com/LSchmidt-TUBS/ADSbDataParser

 

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