DaFne: Data Fusion Generator and Synthetic Data Generation for Cities

In the planning of smart cities, machine learning models can support decision-making with intelligent insights. But what data sets should training processes be based on if there is not yet a city from which to collect data, or if data is not usable due to privacy issues? Synthetic data can provide a realistic representation of conditions in the city not only for machine learning experts, but also for smart city experts. For data-affine users, there are machine learning-based methods for generating synthetic data, but these have limited accessibility to data amateurs. The Platform Data Fusion Generator (DaFne) project aims to improve the usability of data generation methods for various professions. The platform with its generic functionalities should appeal to users from all domains. This paper refers to the research on how smart-city use cases can be addressed and how complex machine learning based methods can be made accessible through the platform to urban professions. Based on results from user interviews and experiments of a smart city case study, the need for a non-generic platform feature emerges. The Use Case Explorer feature provides users with a simple interface to query pre-trained machine learning models to generate data for a specific use case.