The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and ➂ protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

Devices from the Internet of Things are being increasingly used in everyday life, and they provide a massive amount of data in various formats. While implementing the Smart Cities initiative, these data are integrated and utilized together with the 3D city model for further applications. Based on Open Geospatial Consortium standards, heterogeneous sensor data can be integrated with the Open Geospatial Consortium SensorThings Application Programming Interface. Similarly, the 3D city model data can be stored and exchanged with the Open Geospatial Consortium CityGML format. However, currently, there is no concrete model to integrate these sensor data with the 3D city model using the Open Geospatial Consortium standards. The existing solution for integrating the sensor data into the 3D city model requires an extension or plug-in for adding the data to the CityGML model. In this paper, we introduce the concept of “CityThings” to integrate dynamic sensor data from the Open Geospatial Consortium SensorThings API into the CityGML 3D city models. We demonstrate the implementation of the CityThings concept in the Smart Villages project in the study area of Wüstenrot, Germany, by integrating dynamic sensor data from several systems including solar panels, agro-thermal plants, and weather monitoring sensors to visualize the sensor data with the 3D city model on the web platform. In the future, this concept can be applied to interconnect dynamic sensor data and 3D city model data in other Smart Cities applications.

According to the advances in Information & Communication Technology (ICT), nowadays, the use of Internet of Things (IoT) has become a normal part of daily life. It allows interconnections among a wide variety of devices and sensors such as smartphones, smartwatches, automobiles, or any object with a built-in sensor. However, these devices and sensors are developed by numerous different manufacturers which leads to technology lock-in in terms of data formats and protocols. In order of address this heterogeneity, an interoperable sensor protocol is the need of the hour. To address this, we propose a sensor data management system for monitoring pedelec usage and user fitness level. Using a proof-of-concept prototype the study is carried out in downtown of Stuttgart city. The result of the integrated analyzed data is visualized in 3D digital globe CESIUM.

This paper presents a concept and first glimpse at the development of an urban digital twin framework to estimate and forecast the carbon footprints of urban neighbourhoods, with a focus on household consumption choices, specifically in buildings, food, and transportation sectors, as key emission contributors. Despite constituting nearly three-quarters of global carbon emissions, the influence of household consumption choices on a region’s carbon footprint is often neglected. While assessments at a regional or city scale may prove too broad for targeted mitigation strategies, estimating carbon emissions at the neighbourhood scale can foster sustainable and resilient urban areas. However, challenges arise in estimating emissions at this scale due to the availability of aggregated data, insufficient cross-sectoral data integration, and a lack of practical visualisation tools, causing policymakers to overlook the impact of household choices on neighbourhood carbon footprints. Therefore, the present article provides insights into the ongoing early-stage development of using urban digital twins to model, simulate, analyse, and visualise the impact of household consumption choices on neighbourhood-scale consumption-based carbon emissions. By exploring ”what-if” scenarios, this research also seeks to forecast emission profiles based on how household consumption choices influence a neighbourhood’s carbon emissions under future climatic and demographic conditions.

In this paper, we present a concept for an urban energy dashboard built using different Open Geospatial Consortium (OGC) standards integrated with 3D city models. With cities continuously pressed to cut upon their carbon emissions, it becomes vital to visualize different energy data under one application. This perfectly fits our vision of developing an interactive web-based urban energy dashboard, which shows not only static data but also real-time dynamic data such as measured data from sensors, all integrated into 3D city models. Evolution of the mentioned dashboard is presented using three different case study regions based on data availability and its urban scale: 1) Landkreis Ludwigsburg - A district in Baden Württemberg, Germany showing static urban energy data of specific heating demand 2) Brooklyn - A borough in New York City showing urban energy data of specific heating demand (incl. domestic hot water) over a temporal resolution of 5 years and 3) Wüstenrot - A small municipality in Germany showing real-time dynamic energy data streaming from sensors integrated into a 3D city model. Such an interactive dashboard presents both static and dynamic urban energy data to the citizens and city administration in the most straightforward yet effective manner.