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Jxck-S / plane-notify

Star 1.7k

Notify If a selected plane has taken off or landed using OpenSky or ADS-B Exchange data. Compares older data to newer data to determine if a landing or takeoff has occurred. As well as nav modes, emergency squawk and resolution advisory notifications. Can output to Twitter, Discord, and Pushbullet

plane airport land ads-b adsb takeoff opensky-api opensky-network adsbexchange squawk tcas acas opensky adsbx resolutionadviosry emergency-squawk
  • Updated May 7, 2022
  • Python

Ysurac / FlightAirMap

Sponsor
Star 413

Open source project displaying live aircrafts, ships or trackers on 2D/3D map. Browse through the data based on a particular aircraft, airline, airport, tracker or vessel to search through the database or see extensive statistics. Can use ADS-B in SBS1 format (dump1090, Radarcape,...), VRS, VA (VATSIM, IVAO whazzup.txt, phpvms,...), ACARS (acarsdec, acarsdeco2), APRS, AIS as datasource.

tracker cesium crash metar airport airline flight ship vatsim aircraft ivao acars glidernet phpvms notam modes ads-b sbs vessel 3d-map
  • Updated Nov 24, 2020
  • TSQL

nasa / ML-airport-taxi-out

Star 27

The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

machine-learning airport airport-simulation usg-artificial-intelligence usg-ai-training-data
  • Updated Jan 26, 2022
  • Python

nasa / ML-airport-configuration

Star 10

The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

configuration airport airport-simulation usg-artificial-intelligence usg-ai-training-data
  • Updated Jan 26, 2022
  • Python

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