## 1 Introduction  
The increasing utilization of urban underground space (UUS) worldwide affects the field of construction [1]. This development should articulate with the functional needs of cities [2]. In particular, increasing the UUS density offers advantages to better manage the future energy demand in metropolitan areas [3]. The Underground Sustainable Project Appraisal Routine (Uspear) provides a framework to evaluate and improve subsurface projects [4]. Digital modelling and visualization of underground spatial occupation contributes to better management of the aforementioned challenges, to plan new facilities, to avoid damaging utility lines when excavating and, in consequence, economic losses. The \"Underground project\", financed by Swisstopo and the state of Geneva [5], identified subsurface areas as valuable spaces for construction in densely populated urban areas. It developed first use cases and introduces a structure for the digital representation of subsurface objects. The study showed that available data compared to data needed for use cases are heterogeneous, incomplete and imprecise. Although the Geneva state database is renowned for being the most complete one in Switzerland, it misses, e.g., depth information for most of the utilities, moreover the location and geometry of objects are not as accurate as for surface elements. Interviews with experts in Geneva [6] indicated, that ground-penetrating radar (GPR) can be employed as a non-destructive measurement technology to obtain missing data for existing networks. However, barriers like cost, lack of precision, an important investment in data reprocessing or simply the time needed to obtain all required information, are identified. The UK-based project Mapping the Underworld (MTU) exposes ray tracing [7] as a good candidate to overcome visualizing related challenges. This leads to the conclusion, that verification to increase data quality is, compared to similar activities for surface objects, difficult and costly. In addition, data are stored in multiple different BIM/GIS (Building Information Modeling/Geographical Information System) formats. Use cases employing subsurface information, like energy demand estimation on small scale to assess the return of average building retrofits [8], would benefit from linking BIM and GIS data. Interoperability for BIM systems is supported worldwide by buildingS-MART international, in particular by their Industry Foundation Classes (IFC) standard. [PERSON] [9] proposes to use Web ontology language (OWL) to specify IFC. An OWL representation facilitates the mapping between data models, like BIM/IFC and GIS/CityGML (an open data model and XML-based format). Semantic web technologies are key in interconnecting BIM Infra GIS and energy application areas. Several proposals have been made to provide convergence for BIM and GIS data structures, e.g., by [10; 11; 12]: [PERSON] [12] uses a triple grammar approach: an initial BIM/IFC graph type, a GIS/CityGML target graph type and a correlation graph to match both of them: this approach is graph-driven. [PERSON] [10] treated the conversion of a BIM/IFC towards GIS/CityGML for a building office. The conversion applies a BIM-server to store building data, dedicated mapping for semantics, as well as a series of geometric algorithms to match GIS/CityGML requirements: this approach is model-driven. His methodology has been validated on a general architectural model containing complex geometries. [PERSON] [11] developed a CityGML ADE (application domain extension) to proceed with the conversion on a GIS/CityGML platform: this approach is system-driven. The conversion strategy has been tested in collaboration with the Building and Construction Authority (BCA) of Singapore. Structuring real-world objects for digital representation is a fundamental requirement: [PERSON] and [PERSON] [13] present a framework for the description and depth management of heterogeneous datasets of underground utilities. They use an ontology, relying on four linked sub-ontologies.