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Adding a Third Dimension to the Mapping Agencies’ Dominant Data Environment

 

 

 

In 2017, 11 European public mapping agencies (PMAs) financed a EuroSDR project to explore the economic value of 3D geoinformation. They benefited from being able to share knowledge with one another about the findings and their concerns. For the investigated cases, the cost-benefit ratio was found to be about 1:3. Since the calculated financial benefits were rather circumstantial, however, the academics involved in the project complemented the study with a scientific article in which they attempt to provide more nuance by focusing on the broader public value. They conclude that, in general, the use of 3D geoinformation is increasingly profitable. For PMAs, future research will be not so much about whether to make the transition to 3D, but more about how to do so.

Public mapping agencies (PMAs) are in a difficult position. After 25 years the production processes for 2D mapping and GIS have finally reached a status of high efficiency, but now the market increasingly needs 3D data and information. New but here-to-stay applications like BIM, smart cities, augmented reality and climate change studies ‘demand’ it. Plus high-tech innovations and pretty 3D visualisations are more effective means of communicating data about the physical and built environments. But most PMAs are reluctant to invest in anything more than large pilots or individual projects. One of the problems is that the public authority that has to bear the costs is not usually the one that enjoys the most benefits. The core competence of PMAs is to produce all the highly reliable geographical data of the territory needed and distribute it in as client-friendly a way as possible within their imposed business model. If the clients’ needs evolve to 3D, the PMAs have to follow, otherwise they risk losing their authoritative position – or do they? That is the key question for many European PMAs, which is why they participated in the EuroSDR project to investigate the economic value of 3D.

Cost-benefit analysis

For the study – with involvement of the company ConsultingWhere – six application fields were selected: forest management, flood management, 3D cadastre and valuation, civil contingency, asset management, and urban planning. Over the course of six different EuroSDR workshops, attended by representatives of the PMAs and the stakeholders of the application fields, value chain analysis was applied. Improved planning processes were a clear theme in all of the fields. Two application fields were selected for quantification using cost-benefit analysis: flood management, due to the ubiquity of the problem and its high political profile, and urban planning because 3D geoinformation has a significant potential to contribute to the problems of managing urban growth.

In urban planning, the costs and benefits were evaluated in detail by scaling up and comparing real-world cost estimates from Denmark, a country that uses 3D geoinformation in this field, with the Republic of Ireland (using the comparative land areas), which does not. The benefits are based on the financial impacts that are related to processes in the urban planning value chain:

- Local area planning (LAP) revision and the impact on the planning authority

- Visual impact assessment and the lower costs for developers

- Reduced time for citizens to make LAP submissions and major scheme objections

- General improvements to public-sector efficiency.

After the application of some correction factors, the net value of the cost-benefit analysis for a ten-year period was calculated as 1:2.1 and a net present value (NPV) of €22 million.

In flood management, the same financial model was applied but three approaches were taken to ‘triangulate’ the assessment. Firstly, a cost avoidance method used data from Switzerland, one of the few countries in Europe where the PMA has made a complete switch to 3D several years ago. Estimating the damage avoided since then thanks to the use of 3D geoinformation resulted in a cost-benefit ratio of 1:3.3 and an NPV after ten years of €8.9 million. Secondly, a case study was performed looking at an impact study in The Netherlands where high-resolution height data was published as open data some years back. The results of the Dutch study were compared with the cost-benefit potential of a similar high-resolution 3D digital terrain model in Denmark. The third method applied an adapted business case transfer from the USA’s National Enhanced Elevation Assessment to Belgium. The results of the latter two methods were similar to the Swiss one.

The EuroSDR study concludes: “The cost-benefit analysis in both urban planning and flood management demonstrated that benefits outstrip costs by a multiple of two to three times, even when considering each case in isolation. As further applications of 3D geoinformation are added, additional costs should rise more slowly, whilst benefits should accrue at a similar rate, thereby enhancing the overall rate of return.” The PMAs were very content, both with the study outcome and also with the opportunity to share knowledge about the challenges they face in convincing decision-makers to invest in this innovation.

Public value perspective

3D production and processing costs are approaching the same level as for 2D, but 3D innovations also require time and money to invest in technical infrastructure and in transforming business and operating models. In a climate of budgetary constraints, the economic feasibility of 3D innovation is “a point of consideration”, states the report. Accordingly, to further assist public-sector managers making the case for change, two academics involved in the EuroSDR study – Professor Jantien Stoter (TU Delft, The Netherlands, specialised in 3D and 4D geoinformation) and Professor Joep Crompvoets (KU Leuven, Belgium, specialised in national spatial data infrastructures), both of whom have intimate knowledge of PMAs – sought other means to complement the findings. They collaborated with Dr Serene Ho (KU Leuven, specialised in institutional aspects of geospatial innovation) to explore 3D geoinformation innovation from a public value perspective. Such a perspective on innovation explores the value of it from the users’ point of view. While acknowledging that traditional innovation ideals of effectiveness and efficiency matter, it draws attention to civic objectives like responsiveness to needs, liberty and participation, citizenship and transparency. Re-examining the data collected for the EuroSDR study, a qualitative analysis has been published as a scientific article (in Land, May 2018). It reveals that, in the authors’ experience, proving economic value is vital, but the creation of public value is equally or more significantly a driving factor for transformative innovation “as this conveys social and political currency”.

The article points out that moving towards a model where 3D geoinformation is the dominant data environment for PMAs will undoubtedly yield public value. 3D geoinformation enhances a government’s ability to protect its citizens’ quality of life by providing advanced analytical abilities that result in better living environments and avoiding damage to property. It improves the safety of emergency responders. It helps in planning and securing vital infrastructure. Also, it engenders greater trust in public organisations by fostering greater transparency, confidence and the ability to communicate decisions. The paper describes many examples from the 11 European countries.

Authoritative geodata custodians

Classification of the feedback from stakeholders demonstrates clear potential in financial and strategic aspects, mainly generated through mechanisms of improving the effectiveness of technical products (and, subsequently, various workflows and applications) and enhancing the data environment in which stakeholders operate. The potential for public value creation for PMAs could be even more significant, given the fundamental nature of cadastral data for all other development-related decision-making and the growing importance of sound urban planning. The authors conclude: “Innovation in 3D geoinformation would therefore likely consolidate and advance the PMAs’ position as authoritative geodata custodians and emphasise the role PMAs play in fostering secure and sustainable development. The move to 3D is a better way to meet the evolving nature and scale of their public mandate”.

“I have no doubt that 3D mapping is the near future in a fast-growing amount of applications,” says Jantien Stoter. Joep Crompvoets agrees – albeit a little reluctantly, because he also believes that many so-called new applications function fine, and more simply, with 2D data: “A decision-maker’s heart beats faster when they look at 3D visualisations, and this innovation is a logical evolution. So I agree that we will not get this genie back into the bottle again, although it comes at a price.” In their scientific article they refer to the economic value outcome of the EuroSDR study by stating: “The study found that such innovation was potentially a viable return on investment, perhaps even profitable.” This downplays the cost-benefit ratio of 1:3 actually presented in the study. Both professors explain: “We don’t want to give the impression that the financial benefits for the national or regional mapping organisation are always that manifest, let alone achieve a certain cost-benefit ratio. And it makes a big difference whether the investors’ perspective is aimed at obtaining benefits for the country as a whole, or whether the PMA has to sell the data to be profitable on its own. It certainly seems profitable, but the amount of scientific studies is too limited to be very specific. It is easier to prove that 3D geoinformation brings larger effectiveness of policies, processes and operating environments within and between governments and businesses.”

Back to the future

This has echoes of an earlier time, when digital mapping and GIS found their way from the American defence industry to the national mapping authorities worldwide. There were not many convincing cost-benefit studies proving GIS was profitable, yet the PMAs still had to make the switch because everybody could see that it made so many processes more effective and the technology was here to stay. Stoter and Crompvoets recognise many similarities. They add: “An important difference is that back then the transition was made easy because the initial costs of the national large-scale digital mapping and GIS revolution had largely been paid by the utilities sector. Now the demand is so fragmented that there is no focus for a shared business model.” Also, compared to the analogue way of working until then, GIS was a really disruptive technology; the market understood that serious investments and national coordination were needed. “But we passed the point of no return for the transition to 3D about five years ago,” states Jantien Stoter. “Since we can’t calculate the profit of new technology for new and currently unknown possibilities, we have to stop concentrating on the doubts. Look at examples such as Singapore and large cities in China or the PMA of Switzerland – they made the complete switch to 3D geoinformation and no longer want to be without it.” Joep Crompvoets outlines the plans for future research: “Our investigations will concentrate on how – and not whether – to make the change. We think that mapping of base data in 3D is the most efficient for a country when it is centralised, harmonised and quality controlled by the national mapping agencies. Which steps can be taken? Which priorities work best? And how should it be financed?”. His colleague concludes: “It is about the production of correct, up-to-date 3D data at different levels of detail for different applications, without every user group producing and paying for its own snapshot of reality. We’d better get going – back to the future!”

EuroSDR
EuroSDR is a not-for-profit organisation linking national mapping and cadastral agencies with research institutes and universities in Europe for the purpose of applied research in spatial data provision, management and delivery. Joep Crompvoets is EuroSDR’s secretary-general and chairs the Business Models and Operation Commission. He is a professor of information management in the public sector, senior researcher/consultant and project manager at the Public Governance Institute of KU Leuven, Belgium. Jantien Stoter leads the EuroSDR 3D Special Interest Group. She is a professor of 3D geoinformation at The Netherlands’ TU Delft, Faculty of the Built Environment & Architecture. Prof Stoter also works as an innovation researcher at both Kadaster and Geonovum.

The Quest for the Big Picture

 

 

 

 

 

3D city models have become common geospatial data assets for cities, and can be utilised for tasks including planning, visualisation and decision-making. The field of 3D city modelling is diverse, with a multitude of technical solutions. Even the definition of what constitutes a 3D city model still remains ambiguous. Yet, surprisingly few studies have focused on reviewing the existing activities and assembling the big picture. This article presents some of the key findings from a recent study reviewing major 3D city modelling activities in Finland.

3D city models can be seen as an enabler in the smart city paradigm, operating as a user interface to the modern urban environment and acting as a platform for cooperation and services. Therefore, a high degree of interoperability is expected from city models and from the information systems that host them. Also, 3D city models are expected to support a multitude of applications. This remains a challenging task, still hindered by many issues, such as ambiguities and differences in modelling, differences in perspectives, missing guidelines, challenges in data conversion and georeferencing, and problems in data quality.

Quest for data integration

Data integration is an inherent component in city modelling. 3D city models are commonly built through 3D reconstruction and data integration, by merging photogrammetry or laser scanning data with GIS data such as building footprints and IDs. Also, the use of highly detailed building information models (BIM) as a source of as-planned data remains a significant development topic. Another development direction is the integration of time series and sensor data to the objects in a city model. Rather than being static representations of the environment, inclusion of live data is turning city models into increasingly dynamic artefacts and towards the concept of a ‘digital twin’ of the city.

Expanding field of applications

The application space of 3D city models has expanded hand in hand with the technical development. In addition to the ‘traditional’ use cases in urban planning and zoning, 3D city models are expected to facilitate energy analysis, detailed architectural visualisation, interactive and immersive applications development, and participatory GIS. All this places challenging requirements on the platforms of the 3D city models.

The discussion on 3D city models is further complicated by the amount of different platforms used for 3D city model applications. In addition to the professional GIS/CAD tools, 3D city models are used on game engines and various web-based 3D viewers, such as virtual globes.

Reviewing the major 3D city modelling projects in Finland

In a recent study, the authors took a closer look at some of the 3D city modelling projects in the six largest cities in Finland, using project descriptions maintained by the cities to obtain an overview of the current activities. To support further understanding, local city representatives in the studied cities were interviewed. A summary of the analysed projects is presented in Table 1.

Excess of platforms

Firstly, the studied 3D city models utilised a great variety of platforms. In the 19 studied projects, a total of 13 different platforms were discovered for hosting the models. These included GIS/CAD software (e.g. Trimble Locus and Bentley Microstation), virtual globes (e.g. Cesium, CityPlanner, Sova3D, MAPGETS) and game engines (e.g. Unity, Unreal Engine). Some of the software solutions applied in Finland were more or less unique and have not seen wide adoption globally.

Secondly, the authors noted that in each studied city the maintenance and upkeep of the 3D city models was tied to the statutory base mapping process that heavily relies on the chosen GIS/CAD software. In many cases, the 3D model data maintained by the city itself was used as an input or reference data for virtual globes and 3D game engine-based projects, which focused on specific applications often aimed at broader audiences.

Data quality and accessibility

The studied 3D city modelling projects contained a variety of different levels of detail, ranging from simple block models to highly photorealistic models containing facades modelled in great detail. Fewer than half of the models utilised as-planned information such as BIM or architectural plans. More than half of the city models covered the full regional area of the city.

In addition to data quality, accessibility plays a vital role in 3D city modelling, especially when models are to be used as a basis for developing new applications. Almost two-thirds of the studied city models could be freely viewed via a web-based viewer. This was realised either via separate viewer utility or, in the case of virtual globes, natively by the platform itself. More than half of the city models studied were offered for download over the internet. However, none of the cities offered any original raw data such as Lidar point clouds. This hinders possibilities to freely edit the models or to run additional analysis.

The ideal of a general model

Producing and maintaining a 3D city model requires an interplay of several technical solutions and stakeholders, both of which vary from project to project. Many of the studied cases were likely to serve only a limited number of purposes or to pilot 3D modelling in a limited area. However, the interviews showed that this had not been the original intention in the cities. On the contrary, the common vision was close to the ideal of 3D city models serving as general platforms, including the capability to re-use the gathered data and models.

Further, an increased degree of realism in visualisations, attained with 3D models, was seen as a key element in enhancing the interaction and trust between different stakeholders. This includes the notion of demonstrating impact as well as including all relevant stakeholders in the co-design and decision-making processes.

Stakeholders inside and outside city organisations are a heterogeneous group of users that have varying needs, expectations and views of 3D city modelling. The city interviewees stated that up-to-date guidelines and policies were missing or that the existing guidelines were not followed. Ambiguous terminology, lack of coordination and leadership, and the slow adaptation of standards frequently crippled communication and collaboration. Additionally, the lack of expertise was said to result in an incapacity to recognise the need for 3D city modelling and hence to define the requirements for the modelling projects.

Understanding the big picture

By looking at the characteristics of the studied 3D city modelling projects and literature, it was possible to identify three partially overlapping operational cultures in the 3D city modelling scene: 3D GIS, BIM, and computer graphics. Each of these operational cultures (Figure 2) has a unique perspective on 3D city modelling and they often lead to differing realisations (Table 2).

In the 3D GIS category, the modelling projects typically have a large regional coverage but a limited level of detail in 3D reconstruction. Further, the role of semantic data and connection to the existing city GIS data is emphasised over mere geometric reconstruction. Most of the applications focus on professional uses like city planning, mapping and various geospatial analysis. Projects in the BIM category focus on integrating as-planned data from individual buildings to models of limited regions. Understandably, aspects like model conversion and inclusion of interiors are pronounced. Typical applications in this category include urban planning, building permit processes and architectural visualisation. The projects in the computer graphics category are based on game engines, offering the highest level of photorealism facilitated by advanced real-time rendering. At the same time, models are typically limited to local coordinate systems and they rarely utilise semantic information. Also, the 3D game engine-based models have a clear emphasis on game-like interaction and immersion, with the goal of engaging users to collaborate in and explore the models, even on a pedestrian level.

In the big picture, 3D city modelling is revealed to be a diverse and multidisciplinary field with steadily increasing interest and demand. It is often accompanied with visions of a high degree of interoperability and platform-like characteristics. However, when reviewing the modelling activities more closely, they appear fragmented and fail to reach the broad applicability envisioned.

This is, to some extent, explained by the complexity of the situation. Modelling projects are carried out by people from different fields, using varying tools and aiming for different outcomes. Further, the future uses for 3D city models can be hard to predict – and not only because the user group is heterogeneous with different levels and types of expertise. As an outcome, the quest for a single, completely harmonised 3D city model becomes demanding and laborious. Improving the interplay and communication between people and technologies across these three operational cultures is a key element in advancing the interoperability and creating flexible, demand-driven 3D city models of the future.

Further Reading

Julin, A., Jaalama, K., Virtanen, J. P., Pouke, M., Ylipulli, J., Vaaja, M., Hyyppä, J., & Hyyppä, H. (2018). Characterizing 3D City Modeling Projects: Towards a Harmonized Interoperable System. ISPRS International Journal of Geo-Information, 7(2), 55. https://doi.org/10.3390/ijgi7020055

Managing and Visualising Geospatial Data in a User-friendly Environment

 

 

 

 

 

 

 

The smart city concept is developing very quickly around the world, because it provides a comprehensive digital environment that improves the efficiency and security of urban systems and reinforces the involvement of citizens in urban development. This concept is based on the use of geospatial data concerning the urban built environment, the natural environment and urban services. The successful implementation of a smart city project requires the development of a digital system that can manage and visualise the geospatial data in a user-friendly environment. The geographic information system (GIS) offers advanced and user-friendly capabilities for smart city projects. This article shows how a GIS could help in the implementation of smart city projects and describes its use in the construction of a large-scale model of the smart city.

The ‘smart city’ concept aims at developing a comprehensive system that uses geospatial data to enhance the understanding of complex urban systems and to improve the efficiency and security of these systems. This geospatial data concerns (i) the urban built environment such as infrastructure, buildings and public spaces, (ii) the natural environment such as biodiversity, green spaces, air quality, soil and water, and (iii) urban services such as transport, municipal waste, water, energy, health and education. The smart city concept also aims at transforming the ‘silo-based’ management of cities into a ‘shared’ system that involves urban stakeholders in the design, realisation and evaluation of urban projects.

The emergent technology enables cities to achieve more agile management that improves the quality of life for citizens, enhances the economic development, improves the attractiveness of the city and reinforces the involvement of citizens in the city government. Indeed, the smart city concept provides the city managers with pertinent information about the performance of urban infrastructure and services, as well as users’ feedback. Analysis of this data allows policymakers and city managers to improve the efficiency of the urban system as well as the quality of urban services. This concept is particularly pertinent for the security and resilience of the city. It allows collection of data concerning how the city infrastructure and stakeholders respond to urban hazards. Analysis of this data provides greater understanding of the behaviour of urban systems (infrastructure, public services, emergency response, etc.) during urban crises or disasters, and consequently enables improvements to the city’s capacity to address the resiliency challenges. The smart city concept offers the possibility to confine a local fault and to prevent its spread to larger areas.

Use of GIS in smart city projects

The implementation of smart city projects is based on a number of steps (Figure 1) including the construction of the urban digital model, data collection using the sensing layer, then data analysis, interactive data visualisation and system control. GIS plays a role in these steps, as described below.

Construction of the urban digital model

The first step in the implementation of smart city projects concerns the construction of the urban digital model that describes the components of the urban built and natural environments. For each urban component, the digital model provides the geolocalisation and characteristics (attributes). GIS is generally used for the construction of the digital model of urban ‘horizontal components’ such as urban networks, transport facilities and natural environment, while building information modelling (BIM) is used for the description of ‘vertical components’ such as buildings. The combination of GIS and BIM provides a powerful tool for the construction of the urban digital model with georeferenced data and the visualisation of this data in a user-friendly environment.

Sensing layer

The second step in smart city projects concerns the construction of the sensing layer that transfers urban operating data to the smart city information system. This layer includes sensors used for monitoring urban networks and infrastructures. Data could also be enhanced by images, videos and audio files resulting in the construction of urban big data. Figure 2 shows examples of sensors used in monitoring water and energy utilities. The drinking water system uses automatic meter readers (AMRs) to record water consumption, pressure sensors to record water pressure and water quality devices to track the water quality (turbidity, pH, chlorine, conductivity). The drainage system uses sensors to monitor the water level and flow, water quality (turbidity, temperature, pH, etc.) and pumping equipment. It allows early detection of flood and faults in pumping equipment. The electrical grid uses sensors to measure the electrical tension, current and frequency. It allows early detection of faults in the electrical grid. The district heating system is monitored by sensors to record fluid temperature, pressure and flow as well as the state of the valve. It allows early fault detection and the improvement of the system performance. GIS offers the possibility to visualise the monitoring system as well as the sensors’ characteristics and status. It also provides the possibility to visualise real-time and historical data on GIS maps.

Data analysis

The third step in implementing a smart city project concerns the development of the analytic environment, which converts real-time and historical data into operational data that improves the security, efficiency and quality of urban systems. The analytic environment includes engineering, management and safety software for urban systems as well as advanced digital tools such as artificial intelligence (AI). In smart city projects, GIS provides tools for (i) geospatial data analysis (distance and directional analysis, geometrical processing, grid models), (ii) spatiotemporal analysis, (iii) spatial statistics (spatial autocorrelation and egression), (iv) surface analysis (surface form and flow analysis, gridding and interpolation methods) and, (v) location analysis (shortest path calculation, facility location).

Interactive data visualisation

Interactive data visualisation allows users to interact with the smart city’s components and the stakeholders in a user-friendly environment. Web applications are used to create this interactive environment. The use of HTML popups enables users to access web-based content such as graphics referenced by URLs. The interactive GIS graphic environment allows the visualisation of urban components and sensors maps. Users and managers can utilise these maps to access static and dynamic data concerning urban systems as well as to update the data.

Control layer

Data analysis of historical and real-time data results in commands for the optimal and safe management of urban systems. These commands are transmitted to the control layer, which includes different electronic devices such as smart valves, pumps, motors, switches, breakers and locks. The GIS system allows real-time visualisation of these devices as well as their status. It could also visualise faults in device command.

SunRise smart city project

The SunRise smart city project was aimed at the construction of a large-scale model of the smart city at Lille University’s scientific campus. The campus is equivalent to a small town, with 145 buildings, about 25,000 users and 100km of urban utilities.

The first step of the SunRise project included the collection of asset data about the campus utilities and integration in the SunRise GIS system. The data concerned linear components such as pipes and lines and their attributes (diameter, material, age, etc.) as well as utilities-related equipment such as valves, hydrants, pumps, substations, manholes and tanks. Figure 3 shows the use of GIS for the electrical grid of the campus. The GIS map provides the grid architecture as well as attributes of the grid components including the electrical lines and substations. Figure 4 shows the GIS map of the storm-water system and the relevant equipment (valves, flow regulator, retention tank and lifting station).

The SunRise GIS system also included inspection and maintenance data. Figure 5 shows images of the video inspection of the sanitation system. For each component of this system, the management team can gain access to the maintenance history and reports including images and videos. The team can also conduct geospatial analysis of maintenance data for the optimisation of renovation and maintenance costs.

The monitoring system was also integrated in the SunRise GIS system. Figure 6 shows the smart monitoring of the drinking water network. It includes automatic reading meters (AMRs) for the water supply and consumption as well as pressure meters. The team manager can directly access information about the sensors and visualise the consumption history. The team can also compare the consumption of buildings and use data analysis to show abnormal consumption levels.

Conclusion

This article has presented the use of the GIS in the implementation of smart city projects. Since smart city projects are based on the collection, analysis, sharing and visualisation of data concerning urban systems and services, GIS provides powerful capacities for a successful implementation of such projects. GIS allows smart city managers to utilise a user-friendly and widely used digital system in the management of urban systems. GIS was used in the construction of the SunRise smart city demonstrator at the Lille University campus. The use of GIS facilitated effective cooperation between around 20 young researchers and the campus team management. GIS was used to store, share and analyse data concerning the campus utilities as well as their maintenance and monitoring.

Acknowledgement

The author would like to thank Nitivat Voraditee from Lille University for his contribution to this article.

The Institute of Navigation’s GNSS Software Defined Radio Metadata Standard working group is seeking its second and final public comment on the standard through Nov. 30.

In recent years, there has been a proliferation of software-defined radio (SDR) data-collection systems and
processing platforms designed for GNSS receiver applications or those that support GNSS bands.

For post-processing, correctly interpreting the GNSS SDR sampled datasets produced or consumed by these systems has historically been a cumbersome and error-prone process. This is because these systems necessarily produce datasets of various formats, the subtleties of which are often lost in translation when communicating between the producer and consumer of these datasets.

The GNSS SDR Metadata Standard standardizes the metadata associated with GNSS SDR sampled data files and the layout of the binary sample files.

The standard defines parameters and schema to express the contents of SDR sample data files. It is designed to promote the interoperability of GNSS SDR data-collection systems and processors. The metadata files are human readable and in XML format.

A compliant open-source C++ API for reading metadata and binary samples is also officially supported to promote ease of integration into existing SDR systems.

Comments can be made by going to sdr.ion.org and clicking on “Submit a Comment.” Public comments are being accepted through Nov. 30.