HEPTA (Helmholtz European Partnership for Technological Advancement) – Topic area 3: Smart Cities

HEPTA promotes cooperation between Aristotle University of Thessaloniki (AUTh) and the Karlsruhe Institute of Technology (KIT) in the development of sustainable technologies in the fields of air quality, atmospheric physics, biomass and smart cities. The “Topic area 3: Smart Cities” component focuses on applying machine-learning and modelling techniques to urban phenomena, especially those involving aerosol/gas distribution and use of heterogeneous data sources in city/urban mobility contexts. HEPTA has three stated goals: Establish a formal link between AUTh and KIT for technology development, particularly in climate, energy and environment. Promote young researchers with a strict gender parity proportion. Expand the cooperation between the two institutions with a long-term perspective, aiming to accelerate the transfer of research results into concrete applications with industry.

Develop surrogate/inverse modelling of physical phenomena in urban settings using machine learning (for example aerosol and gas distributions in cities). Employ advanced statistical and machine-learning methods (Gaussian Processes, Bayesian/deterministic neural networks) to model spatio-temporal urban environmental phenomena. Fit (land-use) regression models driven by mobile & stationary low-cost sensors (even un-calibrated, noisy), and combine them with other heterogeneous static/dynamic data sources (traffic sensors, satellite images, news reports) related to cities and urban mobility.

The specific focus on smart-city environmental modelling (air quality, mobility, sensors) rather than only generic smart-city infrastructure. Use of state-of-the-art ML/statistical approaches (Gaussian processes, Bayesian networks) with noisy low-cost sensor data and heterogeneous sources — bridging the gap between data science and urban environmental research. Collaboration between KIT and AUTh enables cross-national, multi-institutional research with a strong emphasis on sustainability and urban innovation. Strong emphasis on gender parity in the scientific offspring of the project, which shows a commitment to inclusive research practices.

By improving modelling of urban environmental phenomena (e.g., aerosol/gas distributions) using heterogeneous, real-world data sources, the project can support better decision making in smart cities (e.g., mobility planning, pollution mitigation). The linkage of sensor networks (stationary & mobile), ML/regression models and urban data sources can enable more responsive, data-driven city infrastructure, contributing to more sustainable, resilient cities. The cooperation framework established may accelerate transfer of research to industry and real-world applications, fostering innovation ecosystems in Germany, Greece and beyond. Contributions to gender-balanced research training help improve diversity in the technical & sustainability research domains.