IFCTERRAIN – A FREE AND OPEN SOURCE TOOL TO CONVERT DIGITAL TERRAIN MODELS (DTM) TO OPENBIM INDUSTRY FOUNDATION CLASSES (IFC)

Digital Terrain Models (DTM) play an important role for digital twins of the built environment. However, if the Building Information Modeling method (BIM) is used, many engineers find it difficult to provide BIM-compliant terrain models. We present a small tool with which classic DTM, which have been created by landsurveyors or geospatial engineers, can be converted into the format Industry Foundation Classes (IFC) in order to be used in openBIM projects. This paper first clarifies the use cases and then goes into detail on possible configurations of the transformation process. With the presented software tool IfcTerrain the user may select different export options concerning IFC object type of the terrain, geometric representation, georeferencing or the annotation with metadata. IfcTerrain is free and open source and was developed in the context of an educational institution.

MODELLING CHANGES, STAKEHOLDERS AND THEIR RELATIONS IN SEMANTIC 3D CITY MODELS

Urban digital twins have been increasingly adopted by cities worldwide. Digital twins, especially semantic 3D city models as key components, have quickly become a crucial platform for urban monitoring, planning, analyses and visualization. However, as the massive influx of data collected from cities accumulates quickly over time, one major problem arises as how to handle different temporal versions of a virtual city model. Many current city modelling deployments lack the capability for automatic and efficient change detection and often replace older city models completely with newer ones. Another crucial task is then to make sense of the detected changes to provide a deep understanding of the progresses made in the cities. Therefore, this research aims to provide a conceptual framework to better assist change detection and interpretation in virtual city models. Firstly, a detailed hierarchical model of all potential changes in semantic 3D city models is proposed. This includes appearance, semantic, geometric, topological, structural, Level of Detail (LoD), auxiliary and scoped changes. In addition, a conceptual approach to modelling most relevant stakeholders in smart cities is presented. Then, a model - reality graph is used to represent both the different groups of stakeholders and types of changes based on their relative interest and relevance. Finally, the study introduces two mathematical methods to represent the relevance relations between stakeholders and changes, namely the relevance graph and the relevance matrix.

TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION

To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

IFC-CENTRIC VEGETATION MODELLING FOR BIM

There has been a growing interest in integrating vegetation into the built environment in order to ameliorate the negative effects of increasing urbanisation. In Singapore, government policies encourage the inclusion of skyrise greenery into new and existing buildings. To further streamline workflows, statutory BIM (Building Information Modelling) submissions in architecture, engineering and construction (AEC) industries have been mandated. However, landscape plans are still excluded from these BIM submissions due to the lack of a centralised vegetation database and the absence of a standardised BIM format for landscape architectural submissions. This paper presents a streamlined methodology for creating and using a centralised vegetation library for landscape architects. The workflow leverages off the Industry Foundation Classes (IFC) standard for data exchange regardless of the BIM authoring software used and provides a framework of four operational modules: an expandable and low-maintenance species-level vegetation library, a BIM authoring workflow that allows inclusion of vegetation objects, an IFC interface, and a lightweight 3D vegetation model generator. This paper also showcases a use-case of embedding information-enriched 3D vegetation objects into a simulated landscape plan. The proposed workflow, when adopted in AEC industries, will enable governing agencies to track diverse greening efforts by the industry and to potentially include other measurements such as cooling performance or maintainability.

CITYGML APPLICATION DOMAIN EXTENSION FOR 3D STRATA REPRESENTATIONS IN THE SMARTKADASTER SYSTEM: TOWARDS BEYOND CADASTRE PURPOSE IN MALAYSIA

Recent advancements in 3D city modelling and emerging trends in implementing and realising Digital Twins motivate the Department of Survey and Mapping Malaysia (JUPEM) to develop and implement SmartKADASTER (SKiP) Phase 2. SmartKADASTER Phase I was a precursor to this system, and it primarily focused on applying two-dimensional (2D) spatial data for 3D spatial analysis. CityGML was used as the data model for various Levels of Detail (LoD) in this new initiative to represent city models across the Greater Kuala Lumpur region. SmartKADASTER however, lacks strata information. Therefore, to integrate strata information into the SKiP citymodel environment, an Application Domain Extension (ADE) for CityGML has been developed to convert existing Strata XML to StrataGML, a CityGML-compliant data output format. This paper describes the purpose of the SmartKADASTER initiative in Section 1. Section 2 explains additional context for the initiative as well as some backgrounds. Section 3 discusses the conversion workflow and ADE definitions, followed by a brief discussion of visualisation in Section 4 and a project summary in Section 5.

USING SIMULATION DATA FROM GAMING ENVIRONMENTS FOR TRAINING A DEEP LEARNING ALGORITHM ON 3D POINT CLOUDS

Deep neural networks (DNNs) and convolutional neural networks (CNNs) have demonstrated greater robustness and accuracy in classifying two-dimensional images and three-dimensional point clouds compared to more traditional machine learning approaches. However, their main drawback is the need for large quantities of semantically labeled training data sets, which are often out of reach for those with resource constraints. In this study, we evaluated the use of simulated 3D point clouds for training a CNN learning algorithm to segment and classify 3D point clouds of real-world urban environments. The simulation involved collecting light detection and ranging (LiDAR) data using a simulated 16 channel laser scanner within the the CARLA (Car Learning to Act) autonomous vehicle gaming environment. We used this labeled data to train the Kernel Point Convolution (KPConv) and KPConv Segmentation Network for Point Clouds (KP-FCNN), which we tested on real-world LiDAR data from the NPM3D benchmark data set. Our results showed that high accuracy can be achieved using data collected in a simulator.

VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.

A CONCEPTUAL MODEL FOR IFC-BASED DELINEATION OF CONDOMINIUM RIGHTS IN TURKEY: INITIAL EXPERIMENTS

Owing to the increasing existence of multistorey buildings and infrastructures in the built environment, there is a need for three-dimensional (3D) land administration systems (LAS). Regarding this, condominium rights in real-estate properties are needed to be represented as 3D for preventing misinterpretations with regards to who is responsible for or has ownership in which parts of the buildings. Digitalizing the public services appears in current strategies of governments and administrations since it contributes to transparency, speed, and accurateness in the processes. Building permitting that contains obtaining the occupancy permit is a vital one of these public services. With the even-increasing adaptation of Building Information Modelling (BIM), a whole raft of Building Information Models (BIMs) are created to use in digital building permitting. Thus, a significant opportunity for 3D delineation of condominium rights comes out of the reuse of these BIMs, especially their Industry Foundation Classes (IFC) data. In this sense, this paper puts forward an approach that includes developing the conceptual model to depict condominium rights and linking that model with the IFC schema. The applicability of the approach is demonstrated by using a floor of a simple building. The study shows that IFC-based representation of condominium rights can be beneficial for the transition to 3D LAS in Turkey.

TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS

Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.

BIM-GIS INTEGRATION FOR TRAFFIC SAFETY IN CITIES

This paper reports on the results and further extensions of a concept project that was financially supported by VINNOVA (Sweden’s innovation agency). The project aims, by integrating BIM and GIS, to support traffic safety and contribute towards decreasing the probability of car accidents in general and car-bicycle in more specific. The main objective of the project was to investigate different technologies that support the out- and indoor navigation of moving objects on building and geospatial city models based on BIM and GIS, and to present real life objects which help traffic users in taking better decisions. The concept study had a consortium of six partners (academic and industrial). The project resulted in a proposed solution to be implemented in the ongoing extension of the same project. The proposed solution is argued to be comprehensive that utilises BIM-GIS integration, their capabilities and Real Time Positioning Services (RTPS) for smart cities’ applications. Beside its scientific impact, it can be strongly argued that the proposed solution has a high potential for social-economic impact in creating the awareness and framework for automotive, light-weight vehicles manufacturers and automotive appliances suppliers to collaborate in facing this type of rising traffic problem. In addition to that, the open-source nature of the project will encourage different industrial parties to participate and re-utilize the project’s deliverables in new methods and ideas.