SynthAIr

The availability of a wide variety of labelled data (e.g. safety related) is major bottleneck impeding the accuracy of AI models developed for ATM. One possible solution is to generate synthetic data from original data and a model that is trained to reproduce the characteristics and structure of the original data. The project will explore and define AI-based methods to generate synthetic data. These methods are attractive since they require less user knowledge expertise and better generalisation capabilities. The project will take advantage of advances in computer vision and language technology to develop a universal time series generator (UTG). The generator can be trained on a certain set of data obtained from a small number of airports in order to generate synthetic data about a new airport. This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon Europe research and innovation programme under grant agreement No 101114847. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or SESAR 3 Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them.