Modeling

The Research Cluster "Smart Communities and Technologies" (Smart CT) at TU Wien will provide the scientific underpinnings for next-generation complex smart city and communities infrastructures. Cities are ever-evolving, complex cyber physical systems of systems covering a magnitude of different areas. The initial concept of smart cities and communities started with cities utilizing communication technologies to deliver services to their citizens and evolved to using information technology to be smarter and more efficient about the utilization of their resources. In recent years however, information technology has changed significantly, and with it the resources and areas addressable by a smart city have broadened considerably. They now cover areas like smart buildings, smart products and production, smart traffic systems and roads, autonomous driving, smart grids for managing energy hubs and electric car utilization or urban environmental systems research.

3D spatialization creates the link between the internet of cities infrastructure and the actual 3D world in which a city is embedded in order to perform advanced computation and visualization tasks. Sensors, actuators and users are embedded in a complex 3D environment that is constantly changing. Acquiring, modeling and visualizing this dynamic 3D environment are the challenges we need to face using methods from Visual Computing and Computer Graphics. 3D Spatialization aims to make a city aware of its 3D environment, allowing it to perform spatial reasoning to solve problems like visibility, accessibility, lighting, and energy efficiency.

The aim of the project is to increase the resources- and energy efficiency through coupling of various digital technologies and methods for data capturing (geometry and materials composition) and modelling (as-built BIM), as well as through gamification.

Collaborative project with several companies and institutes.

Harvest4D investigates the whole acquisition, modeling and rendering pipeline for incidental data capture.

This project aims at analysing the traffic flow of open roads in real time by a sensor fusion of radar data with video feeds. Radar gives an accurate position and velocity of vehicles making computer vision methods more robust in computing their spatial extent and classification from video streams. In this way the huge amount of raw data is reduced to semantically relevant information, which is highly memory efficient, anonymous and sufficient to reconstruct traffic flow over long time periods. Another important goal is a sophisticated 3D visualization of the reconstructed traffic flow providing interactive tools for visual analysis. Information obtained in this way will significantly contribute in adopting measures to increase traffic safety.