Very important:

In order for urban planners or decision makers to determine long-term goals regarding land use management, detailed virtual models of real word entities are helpful (Sindram & Kolbe 2014). A high positional accuracy of these objects with precise absolute coordinates is necessary to ensure accurate assessments. Ross (2010) states that information related to land management can be integrated into virtual city models. Cadastral accuracy is necessary in order to plan different scenarios und evaluate existing land uses.

Very important:

Geometrically accurate street space models can be used to calculate sealed surfaces. Ross (2010) gives several examples on how 3D city models can be beneficial for land use analyses, e.g. ratio of building area to roads or green spaces, or evaluating the sealing degree of an area. Other simulations such as visibility, solar potential or noise emissions can also be factors for land use and urban planning and rely on detailed geometric representations of city objects such as roads. Detailed areal representations of street space objects in combination with information on pavement conditions can also allow for potential repair cost analyses. 

Important:

It is important to be able to distinguish between different thematic street space objects in order to make assessments regarding urban planning and land use management. The distinction between road surfaces used for cars and sidewalks used by pedestrians for example should be possible. For low-scale analyses however a more basic identification of space used for transportation might be sufficient.

Important:

Land use management and urban planning rely on up to date spatio-semantic information. This not only includes current geometric outlines of streets of public places but also information on when infrastructure was last maintained.

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of land use management.

Very important:

Big construction projects need to be described in different stages of planning; it should also be possible to create different planning scenarios in order for urban planners to make the best decision. Future developments regarding land use and its consequences on factors such as life quality should be possible. The modelling of highly dynamic and time-varying attributes as well as version management also is possible within semantic 3D city models (Chaturvedi & Kolbe 2016) (Chaturvedi et al. 2017a). 

Important:

Ross (2010) states that virtual 3D city models can be used to visualize and explore realistic representations of urban environments. This is not limited to buildings but can also be expanded to street space models. Realistic visualizations can also be helpful to minimize potential resistance of citizens against planned constructions.

Important:

Detailed street space models can be used to store information on pavement conditions. In combination with accurate representations of real world objects such as roads or public places, potential repair costs as well as maintenance plans can be evaluated. These plans and calculations may not depend on the highest positional accuracy possible. Zhao et al. (2019) show advantages of spatial models in the context of estimating road degradation parameters. 

Important:

Geometrically accurate street space models can be used to evaluate potential repair costs. In order to make first assumptions on future costs, a coars model may be sufficient. Zhao et al. (2019) show advantages of spatial models in the context of estimating road degradation parameters.  

Important:

Maintenance tasks may vary depending on different street space objects such as road surfaces (carriageways) or sidewalks used by pedestrians. Thus a thematic distinction between thematically different objects is desirable.

Important:

Maintenance applications rely on up to date information. This not only includes current geometric outlines of streets of public places but also information on when infrastructure was last maintained.

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of maintenance tasks.

Neutral:

Most important to maintenance tasks is the current state of roads or pavement conditions. Time dependent scenarios are not essential.

Neutral:

Zhao (2019) shows how visualizations of pavement conditions can assist a quick and intuitive way to identify problematic areas.  

Very important:

3D city models and especially accurate representations of street space can provide essential information for different aspects of disaster management. The quality of this information is decisive for planning and executing rescue operations. This applies to spatial resolution, geometric accuracy, topological consistency as well as spatial dimensions of the data (Kolbe et al. 2008). Rupprecht et al. (2011) described ways to conduct simulations of pedestrian flows. Such simulations need the exact 3D representation of navigable surfaces to identify bottle-necks.

Very important:

3D city models and especially accurate representations of street space can provide essential information for different aspects of disaster management. The quality of this information is decisive for planning and executing rescue operations. This applies to spatial resolution, geometric accuracy, topological consistency as well as spatial dimensions of the data (Kolbe et al. 2008). Roads normally closed for cars but wide enough to be used in emergency situations (e.g. by police or ambulance cars) could be identified with geometrically detailed street space models.

Important:

While geometric and positional accuracy of street space objects is of great importance concerning emergency response scenarios, thematic separations of individual objects (sidewalk, carriageway etc.) is not that important.

Very important:

Emergency response applications rely on up to date information on street space objects.

Very important:

3D city models and especially accurate representations of street space can provide essential information for different aspects of disaster management. The quality of this information is decisive for planning and executing rescue operations. This applies to spatial resolution, geometric accuracy, topological consistency as well as spatial dimensions of the data (Kolbe et al. 2008).

Neutral:

Planning and executing emergency response scenarios don’t need different time dependent scenarios but depend on accurate representations of current street space.

Neutral:

Visualization of street space is of no direct significance for emergency response. However, it might indirectly be useful to create rescue maps visualizing emergency gathering points.

Very important:

Labetski et al. (2018) presents needs in road modelling of governments with regard to tasks such as de-icing or weed control. Accurate street space models can assist other municipal tasks including winter road clearance or lighting. These models need to have good positional accuracy.

Neutral:

Labetski et al. (2018) presents needs in road modelling of governments with regard to tasks such as de-icing or weed control. Accurate street space models can assist other municipal tasks including winter road clearance or lighting. These models need to have good positional accuracy. Geometric information on the exact shape of individual road surface or sidewalk can be more coarse.  

Important:

Labetski et al. (2018) presents needs in road modelling of governments with regard to tasks such as de-icing or weed control. Accurate street space models can assist other municipal tasks including winter road clearance or lighting. These models need to have good positional accuracy and provide geometric information on the shape of individual road surfaces or sidewalks. This makes thematic separations different street space objects necessary.  

Important:

Up to date information on the extent of street space objects is important for reliable simulations of different municipal tasks.

Very important:

It can be beneficial to know which street space objects lie next to each other however, this information is not essential.

Neutral:

Applications related to municipal tasks don’t need different time dependent scenarios but depend on accurate representations of current street space.

Neutral:

It can be beneficial to be able to visualize street space models to plan and manage municipal tasks such as clearing streets of snow or finding the best locations for street lights.

Very important:

In combination with knowledge about buried utility infrastructures detailed areal street space models can be used to determine which parts of a road would be affected by street excavations (Becker at al. 2012).

Important:

In combination with knowledge about buried utility infrastructures detailed areal street space models can be used to determine which parts of a road would be affected by street excavations (Becker at al. 2012).

Important:

In combination with knowledge about buried utility infrastructures detailed areal street space models can be used to determine which parts of a road would be affected by street excavations (Becker at al. 2012). In this context, it would be beneficial to know which thematic parts of a road are affected (only sidewalks or also carriageway areas).  

Important:

Up to date information on the extent of street space objects is important for reliable simulations.

Very important:

Topological relations between utility network elements are of great importance (Becker et al. 2013). The CityGML UtilityNetworkADE provides concepts for representing topology and connectivity within utility networks (Kutzner et al. 2018).

Neutral:

Utility Network applications don’t need different time dependent scenarios but depend on accurate representations of current street space.

Important:

It can be beneficial to be able to visualize street space models to compare their position with respect to utility networks. This can also be useful for creating location / site plans.

Very important:

Schwab & Kolbe (2019) give a detailed evaluation on requirements to road space models in the context of autonomous driving development. Highly accurate information on lane boundaries or stopping lines could be derived from detailed street space models. Strassenburg-Kleciak (2016) stated that information on street edges can be used in order to increase driving safety.  

Very important:

High geometric resolution can be necessary in order to simulate uneven or bumpy road surfaces. Strassenburg-Kleciak (2016) stated that information on street edges can be used in order to increase driving safety.

Very important:

Schwab & Kolbe (2019) state that traffic areas used by other road users such as pedestrians, cyclists or trams are required for environmental simulations.

Very important:

Information contained within detailed street space models can be used as “ground truth” for automated driving systems trying to understand their environment (Schwab & Kolbe 2019). This requires up to date models.

Very important:

Schwab & Kolbe (2019) show the importance of road-level linear graphs with attributes for routing (e.g. access restrictions etc.). Using a detailed street space model, topological information can also be derived for areal objects such as adjacent streets or sidewalks next to road surfaces. This might also be useful for environmental simulations with other road users such as pedestrians or bicyclists.   

Time dependent attributes of streets such as varying speed limits should be modelled. The modelling of highly dynamic and time-varying attributes as well as version management also is possible within semantic 3D city models (Chaturvedi & Kolbe 2016) (Chaturvedi et al. 2017a).

Unimportant:

Visualization of street space is of no direct significance for autonomous driving applications.

Important:

Ruhdorfer et al. (2018) show how accurate information on street space can be used to derive information needed for traffic simulations. High positional and geometrical accuracy of the used data is of importance in order to achieve reliable results.

Important:

Ruhdorfer et al. (2018) show how accurate information on street space can be used to derive information needed for traffic simulations. High positional and geometrical accuracy of the used data is of importance in order to achieve reliable results. Wilkie et al. (2010) show methods on how to create a geometrically and topologically consistent 3D model from GIS data usable for traffic simulations. 

Important:

Traffic simulations can be conducted for cars as well as pedestrians or other traffic types. Thus, it can be important to be able to distinguish between thematically different areas.

Important:

Depending on the exact application, traffic simulations can require up to date data. However, often traffic simulations are used to evaluate scenarios different to current situations in order to find new or better traffic flow solutions.

Very important:

Ruhdorfer et al. (2018) show the importance of predecessor / successor relations between individual roads or lanes in the context of traffic simulations. Wilkie et al. (2010) show methods on how to create a geometrically and topologically consistent 3D model from GIS data usable for traffic simulations. 

Very important:

Traffic simulations are often used to evaluate different traffic flow scenarios. This requires represent different streets space models. Additionally time dependant attributes such as varying speed limits should be considered. The modelling of highly dynamic and time-varying attributes as well as version management also is possible within semantic 3D city models (Chaturvedi & Kolbe 2016) (Chaturvedi et al. 2017a). 

Neutral:

Detailed street space models can be used to visualize traffic simulation data. However, an appealing visual representation of traffic simulation data is not the main focus.

Unimportant:

While driving dynamics simulations mostly rely on a high geometric resolution in order to simulate uneven or bumpy road surfaces, and accurate representation of absolute coordinates is (in most cases) not relevant.

Very important:

Driving dynamics simulations mostly rely on a high geometric resolution in order to simulate uneven or bumpy road surfaces. Driving dynamic simulations (see Butz et al. 2004) can also be supported by geometric information derived from detailed street space models.

Important:

Especially in professional motorsports the development and testing of driving dynamics is increasingly based on simulation models. These can also include different thematic surfaces such as curbs (Butz et al. 2004).

Neutral:

In contrast to autonomous driving, driving dynamics simulations do not rely on up to date scenarios. Since these simulations are mostly conducted to test different behaviours of vehicles depending on different surfaces, it is not essential to use existing real world data.  

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of driving dynamics.

Neutral:

Time dependant attributes as well as driving dynamics simulations for different stages of development are of low importance.

Neutral:

Detailed street space models can be used to visualize driving dynamics simulations. However, an appealing visual representation of driving dynamics is not the main focus.

Unimportant:

Randt et al. (2007) describe how virtual 3D landscapes can be used for realistic driving simulators. These driving simulators do not rely on accurate representations of the real world (unless a specific, actually existing route should be trained). Thus, absolute positional accuracy is of minor importance.

Neutral:

Randt et al. (2007) describe how virtual 3D landscapes can be used for realistic driving simulators. A high geometric resolution of individual objects is beneficial.

Very important:

Randt et al. (2007) described how virtual 3D landscapes can be used for driving simulators and emergency driver training.

Unimportant:

Driver training simulations don’t need to be based on real world streets. Street models can be fictional as long as they are realistic.

Important:

Topological information (e.g. which street space objects lie next to each other) can be important in order to simulate other transportation users such as pedestrians or bicyclists that could interact with cars or other vehicles.

Neutral:

Very important:

Randt et al. (2007) described how virtual 3D landscapes can be used for driving simulators and emergency driver training. A realistic visualization is one of the most important aspects of driver training simulators.

Important:

Using LoD2 Buildings in combination with areal street space objects, global, diffuse and direct irradiation values can be estimated. While a relatively high positional accuracy of street space data is required in order to get reliable results, the geometric extent of individual objects doesn’t need to have the highest resolution.

Neutral:

Using LoD2 Buildings in combination with areal street space objects, global, diffuse and direct irradiation values can be estimated. While a relatively high positional accuracy of street space data is required in order to get reliable results, the geometric extent of individual objects doesn’t need to have the highest resolution.

Neutral:

Using LoD2 Buildings in combination with areal street space objects, global, diffuse and direct irradiation values can be estimated. While a relatively high positional accuracy of street space data is required in order to get reliable results, the geometric extent of individual objects doesn’t need to have the highest resolution. It can be beneficial to be able to distinguish irradiation effects for different thematic areas. However information on the position of  an object is more important than it’s semantic function.

Important:

Up to date information on the extent of street space objects is important for reliable simulations.

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of local heat island simulations.

Very important:

The modelling of highly dynamic and time-varying attributes as well as version management also is possible within semantic 3D city models (Chaturvedi & Kolbe 2016) (Chaturvedi et al. 2017a). It is possible to represent varying situations during different times during the day or during the year.

Important:

Accurate and interactive visualizations of solar potential and heat island simulations are important for a quick and intuitive understanding of the results. Detailed information for individual street space objects can be visualized using 3D city models.

Important:

Ghassoun et al. (2014) showed how city models can be useful for air quality analysis. Parameters such as number of intersecting streets, their respective width and angle between street arms can be derived from accurate street space models. In combination with other parts of a city model like buildings and vegetation highly accurate simulations can be conducted.

Neutral:

Ghassoun et al. (2014) showed how city models can be useful for air quality analysis. Parameters such as number of intersecting streets, their respective width and angle between street arms can be derived from accurate street space models. In combination with other parts of a city model like buildings and vegetation highly accurate simulations can be conducted. While good positional accuracy (georeferenced objects) are necessary to achieve reliable results, the exact geometric shape of individual streets, plazas etc. can be more coarse since these simulations are mostly conducted on a larger scale.  

Neutral:

Since fine particle analysis usually are conducted on a larger scale it is not necessary to distinguish between thematically different street space objects such as individual carriageways or sidewalks.

Important:

Up to date information on the extent of street space objects is important in order to achieve accurate results for any spatial analysis method.   

Neurtal:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of fine particle analyses.

Very important:

Neutral:

Important:

Detailed street models with information on elevation can also be the foundation for water run-off and flood simulations with high levels of detail (Schulte & Coors 2009). Accurate absolute positional information as well as accurate relative geometric information on streets can be derived from street space models. In most cases, this information does not need the highest accuracy.

Important:

Detailed street models with information on elevation can also be the foundation for water run-off and flood simulations with high levels of detail (Schulte & Coors 2009). Accurate absolute positional information as well as accurate relative geometric information on streets can be derived from street space models. In most cases, this information does not need the highest accuracy.

Important:

Information on thematically different surfaces can be interesting to evaluate which part of a road a flooding scenario would affect.

Important:

Up to date information on the extent of street space objects is important in order to achieve accurate results for any spatial analysis method.   

Very important:

Important:

Important:

Amirebrahimi et al. (2015) demonstrate flood damage assessment for building models. Similar evaluations could be made using areal street space model. Street space models could be used to visualize flooding scenarios as well as damages.

Important:

Biljecki et al. (2015) state that 3D city models can be used for visibility analysis, to determine the line of sight between two points. This could also be used to find optimal locations for traffic lights or signs. A high absolute positional accuracy as well as a high relative geometric accuracy of streets or sidewalks could be used for this task.  

Important:

Biljecki et al. (2015) state that 3D city models can be used for visibility analysis, to determine the line of sight between two points. This could also be used to find optimal locations for traffic lights or signs. A high absolute positional accuracy as well as a high relative geometric accuracy of streets or sidewalks could be used for this task.  

Important:

Individual traffic lights or signs might be only relevant for a specific group such as car drivers or pedestrians. In order to find optimal locations for these objects, thematically differentiated surfaces can be of significance.

Very important:

Bassani et al. (2015) evaluate GIS data to estimate the available sight distance in a typical urban road. This requires up to date information on street space objects in order to get reliable results.

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of visibility analysis.

Neutral:

Visibility analysis can have different goals depending on the exact application. While the current shape of roads can be important in order to analyse existing visibilities of signs or traffic lights, it can also be important to be able to model different scenarios to find the optimal positioning of city furniture objects.

Important:

The visualization of traffic lights or signs together with a detailed street space model is more important in the context of urban planning and not directly for visibility analysis.

Very Important:

Clearance spaces can easily be generated from road surface data (or sidewalk areas) by extruding these areas by a certain amount (e.g. 4.5m for carriageways, 2.5m for sidewalks). This requires detailed information (coordinates) on absolute positional as well as relative geometric positions of individual surfaces.

Very important:

Clearance spaces can easily be generated from road surface data (or sidewalk areas) by extruding these areas by a certain amount (e.g. 4.5m for carriageways, 2.5m for sidewalks). This requires detailed information (coordinates) on absolute positional as well as relative geometric positions of individual surfaces.

Important:

In order to be able to generate clearance spaces for traffic surfaces used by cars as well as traffic surfaces used by other transportation types (such as pedestrians, trains or ships), thematic distinctions between different thematic surfaces need to be possible.

Very important:

Up to date information on the extent of traffic areas is necessary in order to be able to generated reliable clearance space models.

Neutral:

Topological information (e.g. which street space objects lie next to each other) is not of great importance in the context of clearance space modelling.

Neutral:

Most important to clearance space modelling is the current shape of roads.

Neutral:

It can be beneficial to create visualizations of clearance spaces in order to detect possible conflicts between street space and other city objects such as traffic lights, signs or vegetation quickly. However, simulations such as heavy load transports can also be conducted without a visual representation of the results.