The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B1-2022
30 May 2022
 | 30 May 2022


P. O. Hristov, D. Petrova-Antonova, S. Ilieva, and R. Rizov

Keywords: City digital twin, sensor network, real-time data platform, urban living labs

Abstract. The population density in urban areas is rapidly rising, leading to a constant need for new infrastructure and services for citizens. To reduce the time to implementation and optimise the monetary cost of various solutions, the plans and policies of local authorities and stakeholders would benefit from undergoing a series of virtual stress tests. To this end, prescriptive and predictive technologies are widely adopted to optimise city planning and to understand the urban processes and environment such as air pollution and transportation. Nevertheless, holistic sandboxes tightly integrated with cities are still largely lacking. The city digital twin is a promising concept that provides a tool for exploration of new solutions in a controlled environment before their deployment. The digital twin is a virtual replica of the real city, which collects data from the infrastructure, processes and services using not only the available systems, but also purposely built connected devices and sensors. In this context, the establishment of urban living labs facilitates the monitoring and understanding of urban processes and enriches the digital twin with highly-relevant data. This paper presents an urban living lab, under deployment in the district of Lozenets in Sofia, Bulgaria. It is part of a larger initiative for developing a city digital twin of Sofia to support the design, exploration, and experimentation of different solutions. The living lab is equipped with sensors for monitoring air quality, atmospheric parameters, noise pollution and pedestrian flows. In addition, a Light Detection and Ranging (LiDAR) system is realised as an edge computing facility at one of the busiest intersections of the district. Along with the equipment, the paper describes the architecture and components of the platform for data collection, storage, processing, and visualization. Finally, high-priority studies are presented, and their demographic and economic impact is discussed.