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Semantic segmentation of 3D urban environments plays an important role in urban planning, management, and analysis. This paper presents an exploration of leveraging BuildingGNN, a deep learning framework for semantic segmentation of 3D building models, and the subsequent conversion of semantic labels into CityGML, the standardized format for 3D city models. The study begins with a methodology outlining the acquisition of a labelled dataset from BuildingNet and the necessary preprocessing steps for compatibility with BuildingGNN's architecture. The training process involves deep learning techniques tailored for 3D building structures, yielding insights into model performance metrics such as Intersection over Union (IoU) for several architectural components. Evaluation of the trained model highlights its accuracy and reliability, albeit with challenges observed, particularly in segmenting certain classes like doors. Moreover, the conversion of semantic labels into CityGML format is discussed, emphasizing the importance of data quality and meticulous annotation practices. The experiment as described in the methodology shows that outputs from the BuildingGNN for semantic segmentation can be utilized for the generation of CityGML building elements with some percentage of success. This particular work reveals several challenges such as the identification of individual architectural elements based on geometry groups. We believe that the improvement of the segmentation process could be further investigated in our near future work.

Urban air quality poses a significant public health challenge, particularly in complex urban areas like street canyons, where traffic emissions intensify the issue. The limitations of traditional monitoring methods, marked by a narrow scope and poor visualisation, hinder our understanding of pollutant dispersion dynamics, impeding effective air quality management. Consequently, this study explores the synergy of 3D city models and Computational Fluid Dynamics (CFD) simulation to analyse air pollutant dispersion in complex urban settings. To enhance geo-visualisation, the research employs the next generation of Open Geospatial Consortium (OGC) APIs for data delivery. The integration of 3D city models with CFD simulations and geo-visualisation techniques aims to develop an urban digital twin. This digital twin is envisioned to offer valuable insights into the dynamics of air pollutant dispersion within street canyons, thereby informing more effective air quality management strategies.

Smart cities represent a transformative approach to urban development, leveraging advanced technologies to enhance efficiency and sustainability. Moving into the third decade of the 21st century, smart cities are becoming a vital concept of advancement of the quality of life. Without any doubt, cities today can generate data of high velocity which can be used in plethora of applications. The wind flow inside a city is an area of several studies which span from pedestrian comfort and natural ventilation to wind energy yield. We propose a Visual Analytics platform based on a server-client web architecture capable of identifying areas with high wind energy potential by employing 3D technologies and Open Geospatial Consortium (OGC) standards. The assessment of a whole city or sub-regions will be supported by integrating Computational Fluid Dynamics (CFD) outcomes with historical wind sensor readings. The results, in 3D space, of such analysis could be used by a wide audience, including city planners and citizens, for locating installation points of small-scale horizontal or vertical axis wind turbines in an urban area. The implementation presents various flows for spatio-temporal data processing. A number of evaluation plans assess the system performance. The results show an adequate performance, although there is a lot of room for improvement in future work.

Geological data provide an essential source of information for space planning in the subsurface. Three-dimensional geological models make this information about the subsurface available for various users and use cases. There is a significant demand for subsurface information in city planning, including the modeling of buildings and infrastructures. This demand arises from the interaction between geological processes and the (potential) use of the underground. The paper introduces a concept of a voxel model that includes buildings, traffic infrastructure and the geological structure of the subsurface. The voxel modeling approach aims to provide a unified visualization of geological and urban modeling elements. Furthermore, an analysis of the interaction between buildings and their environment, characterized by geological properties, such as strength of the construction ground or hydraulic and heat conductivity should be feasible. Finally, this paper discusses the feasibility of the concept, based on a case study for the city center of Stuttgart, Germany.

The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems.

Both citizens and authorities play a crucial role in the successful implementation of Positive Energy Districts (PEDs). However, the current public participation methods often fail to facilitate interactive knowledge production in the context of energy flexibilisation and PEDs for the citizens. To close this gap, the DigiTwins4PEDs project will use an innovative public participation process and further develop energy simulation tools using urban digital twins. These tools could address issues such as energy flexibility strategies, including demand-side management, sector coupling, and energy storage options, among others. To support the public participation process, the urban digital twin platform will be integrated into a living lab concept and implemented in different case study regions to co-design energy flexibilisation strategies with citizens. The ultimate goal is to deliver a best-practice catalogue and an action plan for other cities to implement a community-driven transition to PEDs.

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.

This paper presents an investigation into the interoperability of 3D building energy data management, delivery, processing, and visualization via web clients using Open Geospatial Consortium – Application Programming Interface (OGC API) standard-based data models and web interfaces. Specifically, the OGC API – 3D GeoVolumes enable access to 3D city model geometries and semantics on the web, the OGC API – Features support the 2D version of the same geospatial data, the OGC API – Processes are used for CityGML analytics and building energy computation with the SimStadt urban simulation software and the OGC SensorThings API is utilized to manage related spatiotemporal or time-series datasets. The efficacy of this approach has been demonstrated in the OGC Testbed 18 Innovation Program, which highlighted the capacity of OGC API web services to synchronize building energy data and computation results between client and server for the case study of Helsinki, Finland, and Montreal, Canada. The advantages of using OGC API services for 3D building energy data interoperability are discussed, and it is suggested that the use of OGC API be promoted to the general public as well as extended to other domains and on a larger scale in future research.

In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this, this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand and reducing CO2 emissions in the built environment.

Determining emotions of people during different activities (e.g. surfing the web or walking and driving in urban areas) is of high interest to many industries such as the advertising and marketing industry (Imotions, 2022) or city developers and planners (Zeile et al., 2015). However, as the authors have explained in their previous works (Schneider et al., 2020, Dastageeri et al., 2019, Kohn et al., 2018), it is very difficult to determine emotions using surrogate measurements. This is compounded by the fact that many people have trouble to name their emotions correctly as they are often mixed and hardly ever occur as a single and distinct emotion. In order to improve the attractiveness of cities not just based on general presumptions about how citizens would react to certain changes in the urban environment, but based on physical measurements, previous works have shown approaches to do that (Schneider et al., 2020, Dastageeri et al., 2019). They have developed a first attempt to correlate measured physical parameters such as heart rate and skin conductivity (among others) which are triggered by location and environment to emotional states using machine learning. To correlate locations to emotions is an important aspect for city planners, as a person’s emotion for a location defines the personal relationship to a place which can help to gauge the attractiveness of a place and give indicators about where to improve the city or place.

Urban digital twins have become an essential factor for cities and communities to visualize, simulate and analyze data. The conventional geospatial standards work great with online platforms such as CesiumJS or ArcGIS API for JavaScript. However, their usage in different platforms such as game engines has not been well established yet. Game engines provide an interesting application case because they offer a different approach to visualizing large city models and provide a high graphical fidelity.This paper aims to answer how the existing standards, such as the API standards GeoVolumes and SensorThings, as well as the 3D model standards 3D Tiles and Esri Indexed 3D Scene Layer (I3S), can interact with game engines. For this purpose, three use-cases were selected and have been used to build applications. These focus on using sensor data in AR and different city development scenarios in a digital environment.This study shows that different geospatial standard formats such as 3D Tiles, I3S, and GL Transmission Format (glTF) can be used in game engines, either directly or over a GeoVolumes Server. Their implementation makes it possible to use the advantages of game engines with real-world datasets.

Creating an immersive Augmented Reality (AR) experience requires aligning the digital content with the real environment so that the digital content appears and interacts in a similar way to a real object. For that, the virtual object must persist in its position across sessions and be occluded completely or partly, depending on whether a real-world object is in the line of sight. This research makes use of the cutting-edge technology available in the field of AR/MR, the Microsoft HoloLens 2.0, where the in-device Time of Flight (ToF) camera is used to scan the environment to create a Spatial Map. The CityGML LOD1 model enriches the Spatial Map and, in addition, is used as an occlusion mask via a custom rendering pipeline. Spatial Anchors can be tracked using the Simultaneous Localization And Mapping (SLAM) technique, used in conjunction with World Locking Tools to anchor the whole scene to the real-world coordinates. The developed occlusion effect is used in urban planning scenarios to introduce a new area design featuring a car-free environment. The proposed method is evaluated based on performance indicators, including frames per second (FPS) since it is highly correlated to user comfort. The findings of the users’ study demonstrate that the occlusion effect achieves its purpose since most of the participants reported enhancements in the depth perception and overall experience by enabling the occlusion screening.

The relevance of 3D cadastre is increasing from time to time as buildings become more complex. Modeling and storing the physical structure with all its elements in a standardized file format improve its usability in different sectors, reduces data redundancy, creates data consistency, maintainability and scalability would be more effortless. In cadastre, showing the legal right of a property is as important as showing the physical right. Especially when a property is multiple owned and has several uses where each use and owner have different rights, responsibilities, and restrictions. The current cadastral of Addis Ababa is a 2D parcel-based mainly focused on registering ownership and physical data. The system lacks to show overlapping ownerships and uses of multistorey buildings. This study solved this critical short come.The primary goal of this study was to use CityGML to represent the physical and non-physical boundaries of building units. Prior to the release of CityGML 3.0, CityGML 2.0 was widely utilized in this field. However, there are several legal boundary modeling flaws in CityGML 2.0. Contrarily, those gaps were closed in the revised edition. Because of this, CityGML 3.0 was used in this investigation. Firstly, a sample building model was created and tested to demonstrate the concept sample. Then, the concept was realized on a model of an actual condominium building from the study area, Addis Ababa. The findings of the study demonstrate how CityGML 3.0 may be utilized for various 3D cadastre applications, including modeling the legal and physical boundaries of buildings.

As the world has more urbanized, cities need to assess and manage their building energy performances in order to achieve energy-reduction goals. The urban digital twins (UDT) offer promising solutions to this demand by providing valuable insights with qualitative and quantitative information about the building environment. The urban building energy data is not only measured from the equipped sensor devices but can also be simulated based on the analysis software. In this research, we aim to explore the development of the spatial data infrastructure (SDI) for managing building energy in the UDT application by employing the Open Geospatial Consortium (OGC) standards which increases the data usability and efficiency. In our concept SDI, the big data in the UDT application is managed with the OGC specifications as follows: OGC SensorThings API (STA) for data with Spatio-temporal characteristics, OGC API 3D GeoVolumes for 3D geospatial content delivery, OGC CityGML for virtual 3D city models, OGC API Features, Web Feature Service (WFS), and Web Map Service (WMS) for other 2D geospatial contents. This concept enables broad interoperability between multiple data layers and client applications. As a proof of concept, we developed the UDT application called with a highly visual and intuitive user interface using the proposed SDI concept as a part of the EnSysLE project in the study area of three regions in Germany: Ludwigsburg, Dithmarschen, and Ilm-Kreis. The proposed concept can be expanded to other UDT domains and on a larger scale in future work.

Urban platforms are becoming a vital role player in city resources management for achieving the right balance between social and economic services and their impact on the environment. More and more cities are starting to benefit from an urban platform to state the city conditions and re-coin the shape of life depending on data gathered from different city systems. However, urban platforms need further support of data analytics in respect of reaching a smart city platform helping city planners with better decision making. Besides, the majority of operational urban platforms consider 2D data only, missing out on the possible information that could be obtained from 3D city models. This work proposes a concept for an urban platform that supports data manipulation and visualization plus interactive analytic functionalities. As a prototype, an urban platform is implemented for integrating and processing 3D city models and 2D traffic data to derive air pollutants emission rates with analytic visualization, leading urban planning to concentrate on the most affected areas. In addition, interactive features are implemented, including filtering, querying, and classifying data to support the analytic visualization in the developed urban platform.

With the increase of applications using 3D models as base for visualization or simulation the requirements for having valid models rises. While there are many programs that validate models there is no standard for storing the results. The simulation programs also may implement their own validation checking to verify if a model can be simulated which may be error prone or not extensive enough for their purpose. This paper provides an extension for the CityGML format to store the validation result. With the available validation information software using the extension can improve their process of working with models containing errors. CityGML model manufacturers may also improve their creation process of the model as well as deliver them validated.

This paper explains the development of a 3D city model-based Public Participation Platform as a prototype and its implementation in a real-world public participation process to redevelop the Weilimdorf area of Stuttgart city. Alongside conducting Weilimdorf’s public participation process, the goal of the mentioned public participation platform is to research citizens’ acceptance of such tools. The usage of digital tools has become more critical for participation processes. The need for social distancing expedites this change, particularly during the pandemic. Previous research frequently focuses on 2D platforms and smaller sample sizes but nevertheless shows the importance of such tools. However, with current developments in geospatial and web streaming technologies, it has become easier and faster to visualize large-scale 3D city models over the web. In this research, these technologies were used by the citizens of the Weilimdorf area to evaluate the usability of the platform and collect their feedback. The result shows that such a digital public participation platform is a valuable supplement to traditional in-person public participation methods.

The increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities’ environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual Analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities’ air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques’ potential in exploring crucial environmental inside, which is still required. Therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. The time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, Machine Learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors’ locations and predict the future dominant (highly measured) values with location in time as the output. The selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. This work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the increasing environmental problems for big industrial cities could be alarmed and reduced for the future with proposed work.

This paper explains the first insights into the ongoing development of a CityGML based Food Water Energy Application Domain Extension (FWE ADE). Cities are undergoing rapid expansion throughout the globe. As a result, they face a common challenge to provide food, water and energy (FWE) supplies under healthy and economically productive conditions. Consequently, new tools and techniques must be developed to support decision-makers, such as governments, public or private infrastructure providers, investors and city developers, to understand, quantify and visualise multiple interdependent impacts for the sustainable supply of the FWE resources. However, a common practice amongst these stakeholders is to work in their data silos, which frequently results in a lack of data integration and communication between domain specific simulation tools belonging to different infrastructure departments. As a result, insights related to critical indicators showing inter-dependency amongst different urban infrastructure are missed and hence, not included in the cities’ redevelopment action plan. This paper documents the first ongoing attempt by an international group of domain experts from food, water, energy, urban design and geoinformatics to harmonise the data silos of food, water and energy domain for the case study regions of the County of Ludwigsburg in Germany, the city of Vienna in Austria and the neighbourhood of Gowanus in New York, the United States of America.

In the face of climate change and the energy transition that the German federal government is aiming for, all renewable energy potentials need to be tapped. Unfortunately, small wind turbines play a niche role in Germany and most other countries despite the fact, that although they offer advantages as e.g. almost seasonal independent energy production in close proximity to the consumer on the same low-voltage grid level. One reason beside the lower wind speeds that can be expected closer to the ground is, that in comparison to PV (photovoltaic), for which good yield forecasts can be made using global radiation measurements from nearby weather stations or online databases, the yield of small wind turbines, especially in urban areas, can only be forecasted using on-site measurements due to the influence of the surrounding buildings and topography. This method is time-consuming and costly. To address this, within this work a Computational Fluid Dynamics (CFD) simulation based visualization framework for the investigation of the small wind turbine potential is presented. In this specific case the energy supply company EnBW is planning to refurbish the “Neuer Stöckach” urban quarter on the former “Stöckach” company site. As part of the redevelopment, a comprehensive energy concept is planned to integrate renewable energies. In this context the integration of small wind turbines into the energy concept is examined according to this new methodology.

The latest coronavirus (namely severe acute respiratory syndrome coronavirus 2 or COVID-19) was first detected in Wuhan, China, and spread throughout the world since December 2019. To tackle this pandemic, we need a tool to trace and predict trends of COVID-19 at global, national, and regional levels rapidly. Several organizations around the world offer access to COVID-19 related data. However, these data sources are heterogeneous in terms of data formats and protocols as different organizations developed them. To address this issue, a standard way to handle these datasets is needed. In this paper, we propose using the OGC SensorThings API to manage the COVID-19 dataset in a standard form and provide access to the general public. As a proof-of-concept, we implemented a COVID-19 data management platform based on the OGC SensorThings standard named COVID-19 SensorThings or in short COVID-STA. For a use case, we developed a real-time interactive web-based dashboard illustrating the COVID-19 dataset based on the COVID-STA. As a result, we proved that the OGC SensorThings API is suitable to use as a general standard for integrating the heterogeneous COVID-19 data.

As the world continues in the quest to fight global warming and environmental pollution by gradually moving to renewable sources of energy, there is also a need to reduce building energy consumption by refurbishing old and historic buildings to meet the required energy standards. While this approach may differ from city to city across the globe, the refurbishment of old and historic buildings would make a significant impact. That is why it is necessary to educate building owners or occupants by simulating the existing energy consumption and proposing appropriate refurbishment strategies. Because the accuracy of energy simulation is directly proportional to the amount of data available and its reliability, there is a need to find creative ways of supplying incomplete or missing building information. The present paper describes a concept that enables individual building occupants or owners to provide this missing information. Implemented and tested with the 3D city model of Aachen, the proof-of-concept enables individual building owners or occupants to perform energy simulations based on energy information supplied.

Emotions are one of the manner humans use to indicate how they feel about a particular event, place or things. To date there is no consensus about the correlation of measured data to an unambiguously defined emotional state. The selection of parameters, their weight and range, which derive at an emotion, are not clearly defined. Especially, if measurements took place outdoors and during a physical activity. This work is based on previous work and focuses on the parameters and methods to classify measured data to an emotional state. We took a closer look to the values, defined ranges for parameters and performed further pre-processing steps. Furthermore, we revised the assignment of an emotion, analyzed the parameter weights and their correlation. Moreover, we compared our previous approach with further Machine Learning (ML) methods. The results are in line with previous work, however, indicate the need for more and heterogeneous data to endorse the outcome. Further results from the parameter analysis suggest an importance of the skin conductance level (SCL) depending on the method used.

In this paper various schemes for visualizing geo-spatial data such as Computational Fluid Dynamics (CFD) data are explored. The architecture of a new Smart Cities Platform is presented and examples of the visualization capabilities are given. Results show that scalar and vectorial measurands, such as wind pressure and wind directions, may be presented using the same schemes, however, interpretation of the visualization varies between measurands. A hex-grid representation of the highly dense point cloud data yields easier interpretation of the scene as do streamlines for visualizing a path of flow over and around buildings. Results of performance evaluations suggest that the same visualisation scheme (e.g. hex-grid) but different data formats, yields faster loading times when using 3D Tiles rather than GeoJSON and an overall smoother interaction within the application.

The recent advancement in Information & Communication Technology (ICT) is seen as a critical enabler to design intelligent smart cities targeting different domains. One such domain is modes of transport in a city. Currently, various cities around the world are envisioning innovative ways to reduce emissions in the cities by increasing physically active mobility. However, there is still limited information about the safety of cyclists and pedestrians within city limits. To address this, we develop a 3D web-based safe routing tool called Vision Zero. Our concept prototype used Augsburg city, Germany, as a case study. The implementation is based on open-source tools. In the back-end, the OGC 3D Portrayal Service standard helps to deliver and integrate various 2D and 3D geospatial contents on a web-based client using CesiumJS. The OGC SensorThings API (STAPI) standard is used to manage historical and real-time open road-incident data from the Federal Statistical Office of Germany. The navigation system is built up based on the routing engine pgRouting, which calculates the safest route based on the mentioned STAPI server and the road-network dataset from OpenStreetMap.

We propose a server-client web architecture identifying areas with high wind energy potential by employing 3D technologies and OGC standards. The assessment of a whole city or sub-regions will be supported by integrating Computational Fluid Dynamics (CFD) with historical wind sensor readings. The results, in 3D space, of such analysis could be used for locating installation points of small-scale vertical axis wind turbines in an urban area.

Human and ecosystem health is affected by the risk of air pollution. A comprehensive understanding of the parameters generating pollution and governing their nature in time is essential to devise functional policies focusing on minimising the concentration of the pollutants. The effect of pollution parameters on meteorological data and existing in between relationships, have been the focus of the researcher’s planning of better city future. Thorough study of resources utilisation is required for contributing to framing effective, sustainable development, government policies management, and advance public services convenience. For protecting the environmental quality, renewable resources like solar and wind are more incorporated in techniques supporting better city planning. This paper considers the hourly time series Particular Matter (PM) PM2.5 and PM10, Nitrogen Oxide (NO), and Nitrogen Dioxide (NO2), and Ozone (O3) along with measured wind flow and humidity. This study’s objective is to assess the temporal seasonality patterns of these parameters in Stuttgart, Germany. The temporal variations over the city center in Stuttgart are analysed using unsupervised approach to perform seasonal hierarchical clustering on a series of parameters NO, NO2, O3, PM10, and PM2.5, wind speed and humidity. Furthermore, the correlations between meteorological and pollution parameters are analysed using the Spearman rank correlation method. Moreover, a dashboard is developed to provide the user desired time frame visualisation of these parameters. Proposed work would provide empirical meaning and seasonality comparison among the above mentioned parameters combined with interactive dashboard support. The analyses of the presented results clearly demonstrates the relationship between air pollutants, wind, humidity together in combine temporal activities frame. Thus, it would help city planner and policies maker with advanced knowledge of seasonality for meteorological and pollution parameters conditions.

Meteorological data and its effect have been the attention of the researchers of the smart city planning for thorough utilization and management of resources, that help in effective government management, convenient public services and sustainable industrial development. Renewable sources of energy like wind, solar, are being integrated into city planning to improve environmental quality. Wind energy is utilized through wind turbines and requires foreknowledge of wind parameters like speed and direction. The aim of this paper is to predict dominant wind speed and direction for time-series wind dataset, that can be incorporated into city planning for selecting suitable sites for wind turbines. This paper proposes three one-dimensional (1D) algorithms using Long Short Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM) for dominant wind speed and direction prediction. The proposed 1D LSTM (1DLSTM), RF (1DRF) and SVM (1DSVM) take successive time values in terms of wind speed and direction as input and predict the future dominant speed and direction, separately. The proposed algorithms are trained and tested using historical wind dataset of Stuttgart and Netherlands respectively. Prediction using 1DLSTM results in total accuracies reaching up to 93.9% and 94.7%, up to 92.8% and 93.8% using 1DSVM and up to 88.7% and 89.3% using 1DRF for speed and direction, respectively. Thus, prediction of wind nature using the proposed algorithms, will give city planners advanced knowledge of wind conditions.

Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.The first Urban Data Management Symposium (UDMS) was held in 1971 in Bonn, Germany, made the choice of hosting the 6th international conference on Smart Data and Smart Cities (SDSC) in Stuttgart a very natural one. SDSC was established in 2016 as the successor of the UDMS, and this year we celebrate the 40th anniversary of the series of symposia and conferences. The SDSC 2021 will be part of the scientific week on intelligent cities at HFT Stuttgart. Together four events were held during the week of 14th – 17th September 2021, and alongside SDSC participants were invited to attend the “Energy, water and food for the cities of the future” conference, the “LIS-City – liveable, intelligent, and sustainable City” workshop, and the mobility day Stuttgart. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2021 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and HFT Stuttgart (the University of Applied Science Stuttgart), and Professor Volker Coors Chaired the SDSC committee.As in previous years, three key conference themes were proposed to represent the Smart Cities: Smart Data (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), Smart People (volunteered information, systems for public participation) and Smart Cities (systems of territorial intelligence, systems for city intelligence management, 3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).This volume consists of 18 papers, which were selected from 41 submissions on the basis of peer review. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects relating to Smart Data. Selected papers tackle different aspects of Smart Cities: transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Alias Abdul-Rahman, Giorgio Agugiaro, Ken Arroyo Ohori, John Barton, Martina Baucic, Filip Biljecki, Lars Bodum, Pawel Boguslawski, Azedine Boulmakoul, Matteo Caglioni, Caesar Cardenas, Eliseo Clementini, Volker Coors, Youness Dehbi, Abdoulaye Abou Diakité, Adil El Bouziri, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Didier Grimaldi, Ori Gudes, Stephen Hirtle, Martin Kada, Lamia Karim, Robert Laurini, Christina Mickrenska-Cherneva, Christopher Petit, Alenka Poplin, Ivana Racetin, Dimos Pantazis, Preston Rodrigues, Camilo Leon Sanchez, Genoveva Vargas Solar, Nils Walravens, Parag Wate, Besri Zineb, Sisi Zlatanova. We are also grateful to the work of the local organising committee at HFT Stuttgart, without whom this conference would not have been possible.

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.

The demand of serving large 3D spatial data, mainly of urban areas, reflects the need of hierarchical data structures for 3D data. During the last years the OGC community standard I3S (Indexed 3d Scene Layer, ESRI) and 3D Tiles (Analytical Graphics, Inc.) emerged in order to deal with this challenge. Conceptually, hierarchical structures for 3D data operate similarly to web map tiles, differing only in the implementation. Although 3D hierarchical formats can transmit arbitrary sized geospatial data, they are not interoperable with consuming/visualization technologies on the client. A series of prototype implementations focus on rendering of hierarchical organized massive 3D data in various web client technologies employing the 3D Portrayal Service. As a result, the user can query a scene via the 3D Portrayal Service by specifying a spatial region, rather than a specific resource via a URI. The result is delivered either using I3S or 3D Tiles as a data delivery format, depending on which data is available for the specified region. The client APIs are capable of rendering either the I3S or the 3D Tiles content.

Cities become increasingly populated, which calls for new approaches to ensure that cities continue being viable places for citizens to live in. The focus of these approaches should be on understanding citizens regarding their feelings, needs and behaviours. This includes an understanding of the perception of and the emotional reactions to urban structures from citizens’ points of view. Following the approach of urban emotions (Zeile et al., 2005), different objective physiological and subjective self-report measures were used in an experimental study in order to capture these emotional responses and to visualize the data in an emotional map. A small sample (N = 13) of students was asked to collect positive as well as negative hot spots in a park area in the city centre of Stuttgart, i.e. spots that elicit positive or negative reactions. The results show the general potential of the park to function as a recreational area, but also identify room for improvement (e.g. concrete structures in the park). While physiological measures are useful to capture subtle emotional responses in larger areas, subjective measures seem to be more useful for understanding the reasons of the emotional responses by identifying positive as well as negative hot spots. A visualization tool introduced in this paper allows urban planners and other stakeholders (e.g. citizens, tourists) to view the results and analyse the data in an accessible way.

Geo-Visualisation (GV) and Visual Analytics (VA) of geo-spatial data have become a focus of interest for research, industries, government and other organisations for improving the mobility, energy efficiency, waste management and public administration of a smart city. The geo-spatial data requirements, increasing volumes, varying formats and quality standards, present challenges in managing, storing, visualising and analysing the data. A survey covering GV and VA of the geo-spatial data collected from a smart city helps to portray the potential of such techniques, which is still required. Therefore, this survey presents GV and VA techniques for the geo-spatial urban data represented in terms of location, multi-dimensions including time, and several other attributes. Further, the current study provides a comprehensive review of the existing literature related to GV and VA from cities, highlighting the important open white spots for the cities’ geo-spatial data handling in term of visualisation and analytics. This will aid to get a better insight into the urban system and enable sustainable development of the future cities by improving human interaction with the geo-spatial data.

In a transformation process to become a climate-neutral city campus, universities have to deal with the sustainable concept. Since “human factor” plays a significant role in the transformation process, providing easy access to environmental data to influence building occupants’ behavior is essential. By utilizing energy-related data without spatial attribute and existing building geospatial data, data visualization in a web browser can be established for both 2D and 3D platforms. Our implementation presents a visualization of indoor sensor measurement data, where the same geospatial data can be used for both 2D and 3D visualizations even though the 3D platform needs an adjustment. Our approach results in a monitoring tool prototype based on visualization of indoor sensors measurement data, which can be accessed easily in a web browser by all building occupants.

Especially in the field of numerical simulations, such as flow and acoustic simulations, the interest in using virtual 3D models to optimize urban systems is increasing. The few instances in which simulations were already carried out in practice have been associated with an extremely high manual and therefore uneconomical effort for the processing of models. Using different ways of capturing models in Geographic Information System (GIS) and Computer Aided Engineering (CAE), increases the already very high complexity of the processing. To obtain virtual 3D models suitable for simulation, we developed a tool for automatic processing with the goal to establish ties between the world of GIS and CAE. In this paper we introduce a way to use Coons surfaces for the automatic processing of building models in LoD2, and investigate ways to simplify LoD3 models in order to reduce unnecessary information for a numerical simulation.

Virtual 3D models of cities are now being extensively employed for the estimation of thermal energy demand at varying spatial and temporal scales. Efforts in preparing and management of the datasets required for the simulations have reached an advanced stage. Thus allowing to perform city scale simulations using simplified thermal energy balance models. However, the uncertainty inherent in datasets and the reliability of their data sources are often not given due consideration. Such consideration to the uncertainty problem would need a paradigm shift in simulation practices from a single value assignment to uncertainty characterization followed by assessment of qualitative and quantitative impact on the simulation results. The proposed study establishes a mechanism to handle the uncertainty arising from the building geometry reconstruction process and its possible consequences on the thermal energy demand calculations.

Recent advancements in technology has led to the development of sophisticated software tools revitalizing growth in different domains. Taking advantage of this trend, urban energy domain have developed several compute intensive physical and data driven models. These models are used in various distinct simulation softwares to simulate the whole life-cycle of energy flow in cities from supply, distribution, conversion, storage and consumption. Since some simulation software target a specific energy system, it is necessary to integrate them to predict present and future urban energy needs. However, a key drawback is that, these tools are not compatible with each other as they use custom or propriety formats. Furthermore, they are designed as desktop applications and cannot be easily integrated with third-party tools (open source or commercial). Thereby, missing out on potential model functionalities which are required for sustainable urban energy management. In this paper, we propose a solution based on Service Oriented Architecture (SOA). Our approach relies on open interfaces to offer flexible integration of modelling and computational functionality as loosely coupled distributed services.

Providing mobile location-based information for pedestrians faces many challenges. On one hand the accuracy of localisation indoors and outdoors is restricted due to technical limitations of GPS and Beacons. Then again only a small display is available to display information as well as to develop a user interface. Plus, the software solution has to consider the hardware characteristics of mobile devices during the implementation process for aiming a performance with minimum latency. This paper describes our approach by including a combination of image tracking and GPS or Beacons to ensure orientation and precision of localisation. To communicate the information on Points of Interest (POIs), we decided to choose Augmented Reality (AR). For this concept of operations, we used besides the display also the acceleration and positions sensors as a user interface. This paper especially goes into detail on the optimization of the image tracking algorithms, the development of the video-based AR player for the Android platform and the evaluation of videos as an AR element in consideration of providing a good user experience. For setting up content for the POIs or even generate a tour we used and extended the Open Geospatial Consortium (OGC) standard Augmented Reality Markup Language (ARML).

In this paper, an analysis of the effect of the various types of resolution involved in photovoltaic potential computation is presented. To calculate solar energy incident on a surface, shadow from surrounding buildings has been considered. The incident energy on a surface has been calculated taking the orientation, tilt and position into consideration. Different sky visibility map has been created for direct and diffuse radiation and only the effect of resolution of the factors has been explored here. The following four resolutions are considered: 1. temporal resolution (1, 10, 60 minutes time interval for calculating visibility of sun), 2. object surface resolution (0.01, 0.1, 0.375, 0.75, 1.25, 2.5 and 5 m2 as maximum triangle size of a surface to be considered), 3. blocking obstacle resolution (number of triangles from LoD1, LoD2, or LoD3 CityGML building models), and 4. sky resolution (ranging from 150 to 600 sky-patches used to divide the sky-dome). Higher resolutions result in general in more precise estimation of the photovoltaic potential, but also the computation time is increasing, especially as realizes that this computation has to be done for every building with its object surface (both roofs and façades). This paper is the first in depth analysis ever of the effect of resolution and will help to configure the proper settings for effective photovoltaic potential computations.

No abstract available.

Geometric quality of 3D city models is crucial for data analysis and simulation tasks, which are part of modern applications of the data (e.g. potential heating energy consumption of city quarters, solar potential, etc.). Geometric quality in these contexts is however a different concept as it is for 2D maps. In the latter case, aspects such as positional or temporal accuracy and correctness represent typical quality metrics of the data. They are defined in ISO 19157 and should be mentioned as part of the metadata. 3D data has a far wider range of aspects which influence their quality, plus the idea of quality itself is application dependent. Thus, concepts for definition of quality are needed, including methods to validate these definitions. Quality on this sense means internal validation and detection of inconsistent or wrong geometry according to a predefined set of rules. A useful starting point would be to have correct geometry in accordance with ISO 19107. A valid solid should consist of planar faces which touch their neighbours exclusively in defined corner points and edges. No gaps between them are allowed, and the whole feature must be 2-manifold. In this paper, we present methods to validate common geometric requirements for building geometry. Different checks based on several algorithms have been implemented to validate a set of rules derived from the solid definition mentioned above (e.g. water tightness of the solid or planarity of its polygons), as they were developed for the software tool CityDoctor. The method of each check is specified, with a special focus on the discussion of tolerance values where they are necessary. The checks include polygon level checks to validate the correctness of each polygon, i.e. closeness of the bounding linear ring and planarity. On the solid level, which is only validated if the polygons have passed validation, correct polygon orientation is checked, after self-intersections outside of defined corner points and edges are detected, among additional criteria. Self-intersection might lead to different results, e.g. intersection points, lines or areas. Depending on the geometric constellation, they might represent gaps between bounding polygons of the solids, overlaps, or violations of the 2-manifoldness. Not least due to the floating point problem in digital numbers, tolerances must be considered in some algorithms, e.g. planarity and solid self-intersection. Effects of different tolerance values and their handling is discussed; recommendations for suitable values are given. The goal of the paper is to give a clear understanding of geometric validation in the context of 3D city models. This should also enable the data holder to get a better comprehension of the validation results and their consequences on the deployment fields of the validated data set.

Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied. This increasing interest in Smart Cities world-wide, along with a growing understanding of the importance of integrating “Smart” data with other data and wider applications for the benefit of citizens, made the choice of hosting the third Smart Data, Smart Cities conference in Delft – in conjunction with three other conferences – a very natural one. Together the four conferences were held during the week of 1st–5th October 2018, and alongside SDSC participants were invited to attend the ISPRS Technical Commission IV Symposium, the 13th 3D GeoInfo Conference and the 6th International FIG Workshop on 3D Cadastres. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2018 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and TU Delft (the Delft University of Technology), and Professor Volker Coors Chaired the SDSC committee. As in previous years, three key conference themes were proposed to represent the Smart Cities: Smart Data (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), Smart People (volunteered information, systems for public participation) and Smart Cities (systems of territorial intelligence, systems for city intelligence management,3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems). This volume consists of 18 papers, which were selected from 34 submissions on the basis of double blind review, with each paper being reviewed by a minimum of three reviewers. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects of Smart Data and Smart Citizens. The selected papers tackle different aspects of Smart Cities: 3D; Citizen Engagement; transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making. The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Giorgio Agugiaro, Maria Antoniabrovelli, Ken Arroyoohori, Martina Baucic, Michela Bertolotto, Pawel Boguslawski, Azedine Boulmakoul, Caesar Cardenas, Ofelia Cervantes, Volker Coors, Isabel Cruz, Vincenzo Delfatto, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Gerhard Groeger, Eberhard Gulch, Jan-Henrik Haunert, Stephen Hirtle, Umit Isikdag, Martin Kada, Snjezana Knezic, Robert Laurini, Liu Liu, Ed Manley, Viviana Mascardi, Marco Minghini, Raul Monroy, Regina Motz, Beniamino Murgante, Marco Painho, Dev Paudyal, Alenka Poplin, Ivana Racetin, Ismail Rakip Karas, Preston Rodrigues, David Sol, Wei Tu, Wei Tu, Genoveva Vargas, Kavita Vemuri, Edward Verbree, Mingshu Wang, Maribel Yasminasantos, Sisi Zlatanova. We are also grateful to the work of the local organising committee at TU Delft, without whom this conference would not have been possible. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018 3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands

A steadily growing number of application fields for large 3D city models have emerged in recent years. Like in many other domains, data quality is recognized as a key factor for successful business. Quality management is mandatory in the production chain nowadays. Automated domain-specific tools are widely used for validation of business-critical data but still common standards defining correct geometric modeling are not precise enough to define a sound base for data validation of 3D city models. Although the workflow for 3D city models is well-established from data acquisition to processing, analysis and visualization, quality management is not yet a standard during this workflow. Processing data sets with unclear specification leads to erroneous results and application defects. We show that this problem persists even if data are standard compliant. Validation results of real-world city models are presented to demonstrate the potential of the approach. A tool to repair the errors detected during the validation process is under development; first results are presented and discussed. The goal is to heal defects of the models automatically and export a corrected CityGML model.

Due to global warming, green-house effect and various other drawbacks of existing energy sources, renewable energy like Photovoltaic system is being popular for energy production. The result of photovoltaic potentiality analysis depends on data quality and parameters. Shadow rapidly decreases performance of the Photovoltaic system and it always changes due to the movement of the sun. Solar radiation incident on earth's atmosphere is relatively constant but the radiation at earth's surface varies due to absorption, scattering, reflection, change in spectral content, diffuse component, water vapor, clouds and pollution etc. In this research, it is being investigated that how efficiently real-time shadow can be detected for both direct and diffuse radiation considering reflection and other factors in contrast with the existing shadow detection methods using latest technologies and what is the minimum quality of data required for this purpose. Of course, geometric details of the building geometry and surroundings directly affect the calculation of shadows. In principle, 3D city models or point clouds, which contain roof structure, vegetation, thematically differentiated surface and texture, are suitable to simulate exact real-time shadow. This research would develop an automated procedure to measure exact shadow effect from the 3D city models and a long-term simulation model to determine the produced energy from the photovoltaic system. In this paper, a developed method for detecting shadow for direct radiation has been discussed with its result using a 3D city model to perform a solar energy potentiality analysis.

In recent years the technology and workflow for producing and management of large 3D urban models has been established and widely been used. Standards such as CityGML enable the modelling and exchange of semantically enriched multi-purpose 3D urban models for applications like urban planning, public participation, environmental simulation and navigation. However, data quality management is essential to control and enhance the quality of these models in order to be able to meet the needs of the aforementioned applications. Quality management should be performed throughout the whole lifecycle of geospatial datasets – from data acquisition to processing, analysis and visualisation. In this paper, we therefore focus on the integration of a quality management software module into a 3D geospatial data server. First results of a prototype system developed at HFT Stuttgart together with Fraunhofer IGD will be presented in this paper as a starting point for further research into the field of quality management of 3D city models.

No abstract available.