OpenAP is a fully open-source aircraft performance and emission model, covering kinematic properties, thrust, drag, and fuel flow rates, all built on open data. The repository provides the OpenAP databases together with a Python implementation for aircraft performance computations.

An open-source flight trajectory optimizer based on direct collocation. It accounts for meteorological conditions, handles individual or combined flight phases, and supports conventional fuel and cost-index objectives as well as climate-based objectives using global warming or temperature potentials.

A Python package that retrieves meteorological data for flight trajectories. It uses Analysis-Ready, Cloud Optimized (ARCO) ERA5 data from Google Public Datasets (derived from Copernicus ERA5), and returns wind, temperature, humidity, and other parameters. Works in both local and client-server modes, with customizable features and pressure levels.

pyModeS is a Python library for decoding and encoding Mode S (including ADS-B) messages. It is an open-source project that receives great support and contributions from the aviation community. This Python package can be imported into an existing Python project, and it is also used as a standalone tool to view and save live traffic data.

An open-source contrail segmentation model in PyTorch. It uses augmented transfer learning, applying image augmentations to a pre-trained ResUNet so the model can be fine-tuned with only a handful of labelled satellite images. A new loss function, SR Loss, further improves detection by exploiting contrail information in Hough space.

A Python library for wind field estimation using the Meteo-Particle model. Indirect wind and temperature measurements are derived from ADS-B and Mode S data and then assimilated into continuous wind and temperature fields, handling the sparse and uneven distribution typical of aircraft-based measurements.

An open-access book for researchers, engineers, and students on decoding ADS-B and common Mode S messages. The first part covers the background (primary and secondary radar, Mode A/C, Mode S, ADS-B) and the hardware and software needed to receive the signals. The 17 core chapters then walk through each message type in detail, with Python examples throughout.

Construct a database to search for aircraft IDs (such as ICAO address, and registrations) and related information. It is built in Python / Flask and with data from Flight Radar 24.

A python library to process and analyze ADS-B flight data. It can reconstruct flight trajectories from ADS-B data and identify flight phases.