Conference Agenda

For useful applications of 3D city models, a ruleset is needed to unambiguously define 3D objects at differentLoDs. Biljecki et al (2016) refined the LoD specification of the CityGML conceptual model to define such a ruleset for buildings.This has indeed reduced the vagueness of the four main CityGML LoDs. However, this refined framework was defined when 3D city models were mainly generated from measurements and observations. In recent years, new ways have been developed to generate 3D data, such as the automated abstraction of BIM models into generalised buildings, which can be integrated in 3D city models to study the impact of their design on the environment and vice versa.

BIM derived output does not fit neatly into the LoD framework of Biljecki et al (2016).The goal of this paper is therefore to evaluate how suitable the framework
of Biljecki et al. (2016) is for the integration of BIM derived models in 3D city
models over LoD0 to LoD3 and to propose refinements accordingly. This is done
by comparing the output of two BIM envelope extractors with respect to the
existing framework. This research is a first step to standardise converted
BIM models at different LoDs to be used in urban applications

The final paper will extend the evaluation and propose refinements of the LoD framework to make it better suitable for BIM-derived models at different LoDs, also looking from the perspective of data requirements of use cases, such as digital building permit and 3D Cadastre.

Digital building models are increasingly available and used as sources to inform geospatial data sets. This poses the requirement to spatially locate building models in the geospatial context. In this paper, we present a case study on transferring georeference during conversion from digital building models in IFC (Industry Foundation Classes) to OpenStreetMap (OSM). First, we provide a condensed overview of how coordinates in IFC's local engineering coordinate systems are related to geospatial reference systems with examples for different constellations. Second, we demonstrate a simple method to enrich IFC datasets with georeferences using existing OpenStreetMap outlines. In the third part we describe two substantially different methods to convert engineering coordinates into geospatial coordinates and show how these methods are implemented in two opensource software packages. Finally, we verify and compare the methods with a set of sample IFC and OSM data.

Digital Twin technology is the tool for monitoring, management and intervening in a timely manner to prevent disasters in urban areas, transforming them into smart cities. By enabling real-time data flow through utilizing a vast network of interconnected sensors and smart devices the functioning of buildings, facilities and utility networks can be monitored, managed and optimized to achieve a more stabilized, fair and sustainable urban environment. Good land management and optimal exploitation can also be achieved. The paper presents an innovative crowdsourced methodology for a fast, low cost and reliable structuring of the necessary geospatial infrastructure for a digital twin of an urban neighborhood by merging and visualizing individual BIMs with available open data, such as open orthophotos and cadastral maps, planning and building regulations, as well as existing land uses, smart devices and metric data derived from the Google Earth Pro and Street View platforms. The above-mentioned data if transferred into an open-source platform, such as Tandem, are ideal for tracking, managing and improving the residents’ living, from an economic, hygienic, safety and ecological point of view. Data derived from smart devices is a prerequisite for predicting and preventing various problems and setbacks.

The AI4TWINNING project aims at the automated generation of a system of interrelated digital twins of the built environment spanning multiple resolution scales providing rich semantics and coherent geometry. To this end, an interdisciplinary group of researchers develops a multi-scale, multi-sensor, multi-method approach combining terrestrial, airborne, and spaceborne acquisition, different sensor types (visible, thermal, LiDAR, Radar) and different processing methods integrating top-down and bottom-up AI approaches. The key concept of the project lies in intelligently fusing the data from different sources by AI-based methods, thus closing information gaps and increasing completeness, accuracy and reliance of the resulting digital twins. To facilitate the process and improve the results, the project makes extensive use of informed machine learning by exploiting explicit knowledge on the design and construction of built facilities. The final goal of the project is not to create a single monolithic digital twin, but instead a system of interlinked twins across different scales, providing the opportunity to seamlessly blend city, district and building models while keeping them up-to-date and consistent. As testbed and demonstration scenario serves an urban zone around the city campus of TUM, for which large data sets from various sensors are available.

The integration of 3D city models and Building Information Models (BIM) in the context of GeoBIM has gained significant attention from both academia and industry. Harmonizing the distinct characteristics and goals of these models is crucial for successful integration. In this paper, we present the development of a plugin for Autodesk Revit, a popular BIM platform, which allows for the incorporation of 3D geo-data encoded in CityJSON. The plugin, published as open source, enables the generation of individual geometries with associated city model attributes as parameters, facilitating the observation of changes made to the geo model on the BIM model. Challenges addressed during development include georeferencing, data format import, handling different geometries, hierarchy of attributes, code optimization, user-friendliness, and enhanced visualization. Future developments involve generating semantically correct models and incorporating the functionality to reproject CityJSON geometries based on acquired coordinate reference system information. The plugin contributes to the seamless integration of geospatial and BIM data, enhancing interoperability and supporting informed decision-making in the AEC and urban domains.