In the Future Cities application theme, GATE provides innovative solutions enabling the city to use technology, data and IoT to facilitate effective planning of infrastructure and services, in order to improve the quality of life of the citizens.
The Smart City concept attracts the attention and interest of many cities, authorities, industrial and research organizations at the national, European and international level. Unfortunately, the current smart city activities are mainly focused on improving the current living conditions in cities and are often related to specific city dimensions such as e-ticketing, smart street lights, pollution reduction, etc. What is beyond the smart city is the city digital twin – an information-rich city presented with intelligent models that support planning, design, simulation and analysis of all city dimensions by leveraging the full potential of Big Data and AI.
The research in Future Cities application theme is focused on three main fields.
Semantically enriched 3D city models foster the development of city digital twins. They provide a digital representation of urban areas and facilitate further analysis and simulations such as particular dispersion, noise and wind simulations, energy efficiency studies and other analysis, which require the architectural design and urban environment building to be placed in a context (e.g. line of sight and shadow analysis, clash detection with cables and pipelines in the underground, impact of wind circulation). The development of semantically enriched 3D city models includes the following research directions:
Semantically enriched 3D city models enable the potential of Geospatial Artificial Intelligence (GeoAI) algorithms and techniques for geographic knowledge discovery from spatial Big Data and beyond. GeoAI combines the strengths of spatial data science and AI to address real-world problems by applying an interdisciplinary approach (scientific fields including computer science, spatial science, civil engineering, architectural design, statistics, ML, etc.). Spatial data science workflow follows the Big Data value chain, including steps for data management, data integration, data modelling and exploratory data analysis, and visualization.
Advanced simulation and visualization methods are developed to forecast the future behaviour of the urban environment and visualize both qualitative and quantitative data by representing social and environmental parameters influencing urban qualities and affecting humans. Simulations assist urban planning by the representation of important urban qualities and risk scenarios such as microclimate, wind comfort, air quality, flooding, noise and electromagnetic field exposure and coverage. A challenge is to find an appropriate level of data visualization (level of detail and level of abstraction) and abstraction of representation without losing the richness of information. Novel methods and tools are elaborated, providing a combination of real-world observations and virtual reality experiments, apply both physiological and behavioural techniques by addressing different senses such as vision, hearing, touch and motion.