Search Results (9)

As indoor space becomes more important in our daily life, the demand to build digital twins for indoor spaces is increasing accordingly. The properties of indoor spaces, however, differ from those of outdoor spaces, and we need to apply different approaches to build indoor digital twins. In our work, we propose a framework for building an indoor digital twin with a use case for hospitals in general and large hospitals in particular, which may be considered as one of the most complicated types of digital twin. One of our goals is to establish a framework for building indoor digital twins based on standards and our framework starts from OGC IndoorGML, which is a standard for indoor data models and encoding schemes for indoor spatial data. In this paper, each step of the framework is presented for the construction of an indoor hospital digital twin focusing on a use case of epidemic analysis of COVID-19 transmission in a hospital. The use case study covers the entire life cycle of the indoor spatial application from requirement analysis, data modeling, and building indoor spatial data to the development of a COVID-19 transmission analysis. Our work represents a use case for indoor digital twins based on the OGC IndoorGML standard and eventually may serve as a framework and reference for building indoor digital twins. As our work is mainly focused on the construction of hospital digital twins, the study on COVID-19 infection model itself is limited in this paper. Improvement of the infection models and validations will be the next step of our work. As HVAC (heat, ventilation, and air conditioning) was not fully considered in our use case, we also expect that it is possible to strengthen our use case by including HVAC for the analysis of airflow dynamics. Full article

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like ‘loc’, ‘indoorgml-lite’, and ‘bloc’ to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems. Full article

Room usage semantics in models of large indoor environments such as public buildings and business complex are critical in many practical applications, such as health and safety regulations, compliance, and emergency response. Existing models such as IndoorGML have very limited semantic information at room level, and it remains difficult to capture semantic knowledge of rooms in an efficient way. In this paper, we formulate the task of generating rooms usage semantics as a special case of room classification problems. Although methods for room classification tasks have been developed in the field of social robotics studies and indoor maps, they do not deal with room usage and occupancy aspects of semantics, and they ignore the value of furniture objects in understanding room usage. We propose a method for generating room usage semantics based on the spatial configuration of room objects (e.g., furniture, walls, windows, doors). This method uses deep learning architecture to support a room usage classifier that can learn spatial configuration features directly from semantically labelled point cloud (SLPC) data that represent room scenes with furniture objects in place. We experimentally assessed the capacity of our method in classifying rooms in office buildings using the Stanford 3D (S3DIS) dataset. The results showed that our method was able to achieve an overall accuracy of 91% on top-level room categories (e.g., offices, conference rooms, lounges, storage) and above 97% accuracy in recognizing offices and conference rooms. We further show that our classifier can distinguish fine-grained categories of of offices and conference rooms such as shared offices, single-occupancy offices, large conference rooms, and small conference rooms, with comparable intelligence to human coders. In general, our method performs better on rooms with a richer variety of objects than on rooms with few or no furniture objects. Full article

Despite the wealth of cultural heritage objects in Indonesia, some of which are considered UNESCO World Heritage sites, more documentation still needs to be done. One of the reasons behind this problem is that the documentation of complex cultural heritage objects is more complicated than most modern objects, which are often more simplistic. This preliminary study aimed to document vernacular heritage buildings in 3D to be used as a conservation and building management tool. The built digital model can then be used as a building assessment tool. The data acquisition method used in this study was a combination of photogrammetry and laser scanner technology. The building model was stored as BIM (IFC model) and then georeferenced before being converted to IndoorGML. The building elements’ information contained in the building model was retrieved as input to analyze the building. This research included analyses for building assessment, natural room temperature, natural lighting, and indoor space and relation. All results from the analysis were used as input to calculate the reliability value of the building using the AHP method. The case study for the heritage building was the house of Prince Arya Denda at Kasepuhan Palace, Cirebon, Indonesia. Full article

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data. Full article

The increasing complexity of modern buildings has challenged the mobility of people with disabilities (PWD) in the indoor environment. To help overcome this problem, this paper proposes a data model that can be easily applied to indoor spatial information services for people with disabilities. In the proposed model, features are defined based on relevant regulations that stipulate significant mobility factors for people with disabilities. To validate the model’s capability to describe the indoor spaces in terms that are relevant to people with mobility disabilities, the model was used to generate data in a path planning application, considering two different cases in a shopping mall. The application confirmed that routes for people with mobility disabilities are significantly different from those of ordinary pedestrians, in a way that reflects features and attributes defined in the proposed data model. The latter can be inserted as an IndoorGML extension, and is thus expected to facilitate relevant data generation for the design of various services for people with disabilities. Full article

Buildings have a multifunctional character, which makes it hard to define just one model for all their diverse functions. As these diverse functions are addressed by actors of different perspectives and domain backgrounds, the possibility to exchange available building information would be desirable. Two main models for the creation of building information are Industry Foundation Classes/Building Information Modelling (IFC/BIM) and City Geography Markup Language (CityGML). As the importance of information interchange has been recognized, several authors have tried to develop intermediate models for the information exchange between IFC/BIM and CityGML, e.g., the Unified Building Model (UBM), the BIM Oriented Indoor data Model (BO-IDM), the Indoor Emergency Spatial Model (IESM) and the BIM-GIS integration model for Flood Damage Assessment (FDA model). Nevertheless, all these models have been created with a certain use in mind. Our focus in this article is to identify common elements amongst these proposed models and to combine them into one “core model” that is as simple as possible, while simultaneously containing all important elements. Furthermore, this base model extracted from proposed intermediate models can then be expanded to serve specific use requirements, while still being exchangeable. To show this cross-domain character of the core model, we validated the resulting model with two cases of use (production environment/maintenance and 3D digital cadaster). Full article

The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which is the semantic engine that fuses big geo-information data, is however, discarded in these standards. The Chinese national standard of Indoor Multi-Dimensional Location GML (IndoorLocationGML) presented in this study can be used in ubiquitous indoor location intelligent applications for people and robots. IndoorLocationGML is intended as an indoor multi-dimensional location information model and exchange data format standard, mainly for indoor positioning and navigation. This paper introduces the standard’s main features: (1) terminology; (2) indoor location information model using a Unified Modeling Language (UML) class diagram; (3) indoor location information markup language based on GML; and (4) use cases. A typical application of the standard is then discussed. This standard is applicable to the expression, storage, and distribution of indoor multi-dimensional location information, and to the seamless integration of indoor–outdoor location information. The reference and basis are therefore relevant to publishers, managers, users, and developers of indoor navigation and location-based services (LBS). Full article

Several tasks in urban and architectural design are today undertaken in a geospatial context. Building Information Models (BIM) and geospatial technologies offer 3D data models that provide information about buildings and the surrounding environment. The Industry Foundation Classes (IFC) and CityGML are today the two most prominent semantic models for representation of BIM and geospatial models respectively. CityGML has emerged as a standard for modeling city models while IFC has been developed as a reference model for building objects and sites. Current CAD and geospatial software provide tools that allow the conversion of information from one format to the other. These tools are however fairly limited in their capabilities, often resulting in data and information losses in the transformations. This paper describes a new approach for data integration based on a unified building model (UBM) which encapsulates both the CityGML and IFC models, thus avoiding translations between the models and loss of information. To build the UBM, all classes and related concepts were initially collected from both models, overlapping concepts were merged, new objects were created to ensure the capturing of both indoor and outdoor objects, and finally, spatial relationships between the objects were redefined. Unified Modeling Language (UML) notations were used for representing its objects and relationships between them. There are two use-case scenarios, both set in a hospital: “evacuation” and “allocating spaces for patient wards” were developed to validate and test the proposed UBM data model. Based on these two scenarios, four validation queries were defined in order to validate the appropriateness of the proposed unified building model. It has been validated, through the case scenarios and four queries, that the UBM being developed is able to integrate CityGML data as well as IFC data in an apparently seamless way. Constraints and enrichment functions are used for populating empty database tables and fields. The motivation scenarios also show the needs and benefits of having an integrated approach to the modeling of indoor and outdoor spatial features. Full article