CityGML in the Integration of BIM and the GIS: Challenges and Opportunities

CityGML (City Geography Markup Language) is the most investigated standard in the integration of building information modeling (BIM) and the geographic information system (GIS), and it is essential for digital twin and smart city applications. The new CityGML 3.0 has been released for a while, but it is still not clear whether its new features bring new challenges or opportunities to this research topic. Therefore, the aim of this study is to understand the state of the art of CityGML in BIM/GIS integration and to investigate the potential influence of CityGML3.0 on BIM/GIS integration. To achieve this aim, this study used a systematic literature review approach. In total, 136 papers from Web of Science (WoS) and Scopus were collected, reviewed, and analyzed. The main findings of this review are as follows: (1) There are several challenging problems in the IFC-to-CityGML conversion, including LoD (Level of Detail) mapping, solid-to-surface conversion, and semantic mapping. (2) The ‘space’ concept and the new LoD concept in CityGML 3.0 can bring new opportunities to LoD mapping and solid-to-surface conversion. (3) The Versioning module and the Dynamizer module can add dynamic semantics to the CityGML. (4) Graph techniques and scan-to-BIM offer new perspectives for facilitating the use of CityG

Enhanced LoD concepts for virtual 3D city models

Virtual 3D city models contain digital three dimensional representations of city objects like buildings, streets or technical infrastructure. Because size and complexity of these models continuously grow, a Level of Detail (LoD) concept effectively supporting the partitioning of a complete model into alternative models of different complexity and providing metadata, addressing informational content, complexity and quality of each alternative model is indispensable. After a short overview on various LoD concepts, this paper discusses the existing LoD concept of the CityGML standard for 3D city models and identifies a number of deficits. Based on this analysis, an alternative concept is developed and illustrated with several examples. It differentiates between first, a Geometric Level of Detail (GLoD) and a Semantic Level of Detail (SLoD), and second between the interior building and ist exterior shell. Finally, a possible implementation of the new concept is demonstrated by means of an UML model

The Energy Application Domain Extension for CityGML: enhancing interoperability for urban energy simulations

The road towards achievement of the climate protection goals requires, among the rest, a thorough rethinking of the energy planning tools (and policies) at all levels, from local to global. Nevertheless, it is in the cities where the largest part of energy is produced and consumed, and therefore it makes sense to focus the attention particularly on the cities as they yield great potentials in terms of energy consumption reduction and efficiency increase. As a direct consequence, a comprehensive knowledge of the demand and supply of energy resources, including their spatial distribution within urban areas, is therefore of utmost importance. Precise, integrated knowledge about 3D urban space, i.e. all urban (above and underground) features, infrastructures, their functional and semantic characteristics, and their mutual dependencies and interrelations play a relevant role for advanced simulation and analyses. As a matter of fact, what in the last years has proven to be an emerging and effective approach is the adoption of standard-based, integrated semantic 3D virtual city models, which represent an information hub for most of the abovementioned needs. In particular, being based on open standards (e.g. on the CityGML standard by the Open Geospatial Consortium), virtual city models firstly reduce the effort in terms of data preparation and provision. Secondly, they offer clear data structures, ontologies and semantics to facilitate data exchange between different domains and applications. However, a standardised and omni-comprehensive urban data model covering also the energy domain is still missing at the time of writing (January 2018). Even CityGML falls partially short when it comes to the definition of specific entities and attributes for energy-related applications. Nevertheless, and starting from the current version of CityGML (i.e. 2.0), this article describes the conception and the definition of an Energy Application Domain Extension (ADE) for CityGML. The Energy ADE is meant to offer a unique and standard-based data model to fill, on one hand, the above-mentioned gap, and, on the other hand, to allow for both detailed single-building energy simulation (based on sophisticated models for building physics and occupant behaviour) and city-wide, bottom-up energy assessments, with particular focus on the buildings sector. The overall goal is to tackle the existing data interoperability issues when dealing with energy-related applications at urban scale. The article presents the rationale behind the Energy ADE, it describes its main characteristics, the relation to other standards, and provides some examples of current applications and case studies already adopting it

FLOOR PLANS IN CITYGML

This paper (1) discusses the modelling of floor plans in CityGML; (2) proposes a delineation of multiple variants of indoor LoD0 in line with the current proposal for CityGML 3.0; (3) demonstrates a method to generate CityGML datasets with included floor plans; and (4) explores their usability. The use of an Application Domain Extension (ADE) is being proposed in order to preserve potentially useful information found within detailed building information models (BIM), specifically Industry Foundation Class (IFC), that cannot be stored in CityGML natively. Our work follows the current developments of CityGML 3.0, and based on the discussions in the CityGML Standards Working Group (SWG) it showcases one of the first datasets consistent with the ongoing development of CityGML 3.0 and one that follows the proposals for a new LOD concept and new interior features

Automatic 3D building model generation using deep learning methods based on cityjson and 2D floor plans

In the past decade, a lot of effort is put into applying digital innovations to building life cycles. 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle. Creating such digital representation of a building can be a labour-intensive task, depending on the desired scale and level of detail (LOD). This research aims at creating a new automatic deep learning based method for building model reconstruction. It combines exterior and interior data sources: 1) 3D BAG, 2) archived floor plan images. To reconstruct 3D building models from the two data sources, an innovative combination of methods is proposed. In order to obtain the information needed from the floor plan images (walls, openings and labels), deep learning techniques have been used. In addition, post-processing techniques are introduced to transform the data in the required format. In order to fuse the extracted 2D data and the 3D exterior, a data fusion process is introduced. From the literature review, no prior research on automatic integration of CityGML/JSON and floor plan images has been found. Therefore, this method is a first approach to this data integration

MIGRATION OF DIGITAL CARTOGRAPHY TO CITYGML; A WEB-BASED TOOL FOR SUPPORTING SIMPLE ETL PROCEDURES

Abstract. Digital cartography is notably produced in all countries, in different scales and formats. Latest cartographic production aims at creating 3D objects with topological consistency and rich information linked by attribute tables, i.e. the principles behind data to be managed in geographic information systems (GIS) environments. These data contain all the information necessary for production of the first levels of detail (LOD) of the CityGML model. The work presented reports on the first steps for a guided workflow to upload cartographic data containing building footprints, heights and other information, and migrating it to a validated CityGML model. The steps include a web-portal for uploading the data in a compressed archive containing shapefiles, and a back-end Python script that reads coordinate vertices, attributes and other necessary information, and creates a CityGML file. The process was tested on the Italian topographic geodatabase of some of the main cities of Italy. Discussion on workflow steps and results are presented. Results show that this process is feasible and it can be used to facilitate first tests on transforming existing cartography to CityGML models, which can be then used for further analysis.</p