Urban digital twins, or virtual 3D city models, have been used for some time now within specific domains. However, until recently it took a lot of effort to generate suitable 3D building models for a given application due to the required manual editing. Therefore, such detailed 3D models are rarely available for large areas (i.e. entire regions or nations) and typically rely on expensive licences and complex data formats. This makes these models hard to access and difficult to use. The limited availability and accessibility of detailed 3D building models hinders the efficient operation of urban areas and limits the economic and societal potential of 3D models. The solution lies in the unique method we developed in the preceding ERC project, which makes it possible to generate high-quality, detailed and easy-to-use 3D building models fully automatically at a national scale and use these 3D data without any technical or legal restrictions.
Such a digital twin of buildings provides a valuable information source for professionals in sectors such as planning, design, construction, energy and real estate. In addition, it offers a simulation basis for environmental impact studies on noise pollution, floods, wind flows, pollutant dispersion, urban heat islands, etc. Without such 3D data, it is at best laborious but often impossible to provide the insights necessary to efficiently maintain the built environment.
This PoC on ‘detailed 3D Building models Automatically Generated for very large areas’ (3DBAG) builds on the ERC project Urban Modelling in higher Dimensions (UMnD) that aimed at developing a fundamental solution for providing 3D data at application-specific Levels of Detail (LoDs). The project achieved several milestones that led to the origin of this idea. First, we have developed a 3D reconstruction method that automatically generates models at multiple LoDs from a combination of 2D data and height points. Second, to serve different urban applications from the same 3D base data, we have identified the 3D data needs for different applications and the reconstruction algorithms have been aligned to those needs. Third, we have developed a file standard (CityJSON) to accommodate such 3D data in a developer-friendly way to enable easy-use of the data by non-experts. This standard has been successfully adopted by the international standardisation Open Geospatial Consortium and is indeed used in practice to introduce our 3D data in new domains.
Before this PoC, the solution was available in a prototype and was used to generate 3D models for all of the 10 million buildings in The Netherlands. This dataset is available as open data since 2021 and is downloaded frequently and used in many applications. This PoC aimed at expanding this prototype to sustain this dataset as a long-term, reliable, up-to-date and user-friendly service that will remain openly available for everyone.
The impact of this PoC project for both professionals and the large public is high. The results enable a breakthrough in the use of 3D data, making it possible for a larger segment of society to benefit from the information 3D city models contain.
