Solar Potential Analysis and Integration of the Time-Dependent
Simulation Results for Semantic 3d City Models Using Dynamizers

Chaturvedi, Kanishk; Willenborg, Bruno; Sindram, Maximilian; Kolbe, Thomas H.

doi:10.5194/isprs-annals-iv-4-w5-25-2017

Cited by 18 publications

(14 citation statements)

References 7 publications

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“…This strategy of creating an artificial sampling point cloud enables the immediate use of point cloud tools and algorithms on the semantic 3D city model and the potentials discussed in subsection 2.5. For instance, (Willenborg et al, 2018, Chaturvedi et al, 2017 present a method for a solar potential simulation based on City-GML models. The application estimates the direct, diffuse, and global solar irradiation on city model features.…”

Section: Point Cloud Data Derived From Semantic 3d City Modelsmentioning

confidence: 99%

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Beil

Kutzner

Schwab

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.

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Section: Point Cloud Data Derived From Semantic 3d City Modelsmentioning

confidence: 99%

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Beil

Kutzner

Schwab

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

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“…The city model is textured according to global irradiation values (kWh/a), ranging from blue (low irradiation values) over green to red (high irradiation values). This type of visualization is useful for quick and intuitive analyses of suitable areas to install photovoltaic systems or, in the case of streets, to locate local heat islands or shady places [82]. In order to allow more profound analyses, all calculated irradiation values as well as attributes, such as a 'Sky View Factor' (SVF), are also stored as attributes for each individual city object.…”

Section: Application Examplesmentioning

confidence: 99%

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Beil

Ruhdorfer²,

Coduro³

et al. 2020

IJGI

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In the context of smart cities and digital twins, three-dimensional semantic city models are increasingly used for the analyses of large urban areas. While the representation of buildings, terrain, and vegetation has become standard for most city models, detailed spatio-semantic representations of streetspace have played a minor role so far. This is now changing (1) because of data availability, and (2) because recent and emerging applications require having detailed data about the streetspace. The upcoming version 3.0 of the international standard CityGML provides a substantially updated data model regarding the transportation infrastructure, including the representation of the streetspace. However, there already exist a number of other standards and data formats dealing with the representation and exchange of streetspace data. Thus, based on an extensive literature review of potential applications as well as discussions and collaborations with relevant stakeholders, seven key modelling aspects of detailed streetspace models are identified. This allows a structured discussion of representational capabilities of the proposed CityGML3.0 Transportation Model with respect to these aspects and in comparison to the other standards. Subsequently, it is shown that CityGML3.0 meets most of these aspects and that streetspace models can be derived from various data sources and for different cities. Models generated compliant to the CityGML standard are immediately usable for a number of applications. This is demonstrated for some applications, such as land use management, solar potential analyses, and traffic and pedestrian simulations.

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“…It allows, on the one hand, representing time-dependent data and sensor data in standardized ways within a CityGML file, and on the other hand, providing a method for injecting dynamic variations of city object properties (like the electricity consumption of a building or the traffic density within a road segment) into the static representations. This concept has already been implemented within the initiative OGC Future City Pilot Phase 1 to support time-dependent solar power potential simulation results and to link real-time observations from Sensor Observation Services with 3D city objects [84,85].…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities

Chaturvedi

Kolbe

2019

Sensors

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Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and is stored in databases and even simple files. To make well-informed decisions, it is essential to have a proper data integration strategy, which must allow working with heterogeneous data sources and platforms in interoperable ways. In this paper, we present a new lightweight web service called InterSensor Service allowing to simply connect to multiple IoT platforms, simulation specific data, databases, and simple files and retrieving their observations without worrying about data storage and the multitude of different APIs. The service encodes these observations “on-the-fly” according to the standardized external interfaces such as the OGC Sensor Observation Service and OGC SensorThings API. In this way, the heterogeneous observations can be analyzed and visualized in a unified way. The service can be deployed not only by the users to connect to different sources but also by providers and stakeholders to simply add further interfaces to their platforms realizing interoperability according to international standards. We have developed a Java-based implementation of the InterSensor Service, which is being offered free as open source software. The service is already being used in smart city projects and one application for the district Queen Elizabeth Olympic Park in London is shown in this paper.

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Solar Potential Analysis and Integration of the Time-Dependent Simulation Results for Semantic 3d City Models Using Dynamizers

Cited by 18 publications

References 7 publications

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities

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