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![Figure 1. Architectural space and its components [1].](publication/334740554/figure/fig1/AS:785980420272128@1564403743788/Architectural-space-and-its-components-1_Q320.jpg)
Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to...
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... indoors are created by carving space out of space, creating space out of space and designing spaces by dividing this space using various tools, such as geometry, colours and shapes. Architectural Spaces are defined based on the form, function and qualities (see Figure 1) and the information captured or available may vary across different designs, making it difficult for machines or computers to interpret. While some of these are syntactic and may be transferable, the semantic components are not well standardized. ...
Citations
... Maheshwari et al. [28] created an ontology for indoor places that considers both semantic and geometric properties. On the basis of this ontology, a space semantic model is developed, which can be used in a variety of applications. ...
... If these spaces can be captured qualitatively and shared as part of the evacuation path to the users, it might make it easier for people movement and re-defining the paths depending on the changing environment in the building. A semantic indoor space model proposed in (Maheshwari and Rajan, 2016) based on combining the ontology for indoor spaces with geometric and semantic characteristics of the space, as defined in (Maheshwari et al., 2019), can be combined with the path generation approach to provide for a more informative and comprehendible path, that can be used by all residents of such buildings. An attempt at using semantic information in indoor path planning is presented in (Xiong et al., 2015) by combining both geometric and semantic information of building components. ...
Accidental fires in public and large buildings not only cause property loss but also can lead to loss of lives.
During such emergencies, building evacuation depends on a range of factors including floor plans, exits
available, obstructions if any, the occupancy levels of the building, and so on. The study here brings together
the spatial, temporal, and path planning possibilities to evaluate fire evacuation strategies for 2D building
plans. It provides a geospatial framework to assess the impacts of dynamic changes in the building
environment and its impact on evacuation outcomes. In this study, occupancy-based path planning using
Pgrouting over an IndoorGML formatted data is combined with modeling their interactions over the path
toward the exit to assess the outcomes. This computational approach over the time-dependent path provides
interesting insights into determining the number of paths and the need for one or more exits during an
emergency. The study shows that integrating the floor plan into path generation and people flow can be a
powerful tool for assessing the building environment.
... In line with common student activities, experimental sites were classified into learning, dining, and residential spaces based on spatial functions (Boukhechba et al., 2018). In order to explore the influence of qualities-related factors on emotion (Maheshwari et al., 2019), such as visual and sound atmospheres, as well as interior furnishings, six typical indoor communication spaces were chosen as the experimental sites and were compared one by one based on their functional classification: classroom (CR), learning corridor (LC), coffee shop (CS), fast food restaurant (FFR), dormitory (DT), and living room (LR). Indoor layouts of the experimental sites are shown in Figures 1A-F. ...
... In terms of the selection of experimental sites, limitations in terms of being able to control variables when comparing two spaces might have caused the results to be confounded by uncontrolled factors. In addition to the variables listed in this research, the spatial scale, dynamism, and indoor partitions are key factors of spatial perceptions and thus impact emotions (Hogg et al., 2011;Maheshwari et al., 2019). For instance, small rooms are considered more pleasant, calmer, and safer than large rooms (Tajadura-Jiménez et al., 2010). ...
Against the background of weakening face-to-face social interaction, the mental health of college students deserves attention. There are few existing studies on the impact of audiovisual interaction on interactive behavior, especially emotional perception in specific spaces. This study aims to indicate whether the perception of one’s music environment has influence on college students’ emotion during communication in different indoor conditions including spatial function, visual and sound atmospheres, and interior furnishings. The three-dimensional pleasure–arousal–dominance (PAD) emotional model was used to evaluate the changes of emotions before and after communication. An acoustic environmental measurement was performed and the evaluations of emotion during communication was investigated by a questionnaire survey with 331 participants at six experimental sites [including a classroom (CR), a learning corridor (LC), a coffee shop (CS), a fast food restaurant (FFR), a dormitory (DT), and a living room(LR)], the following results were found: Firstly, the results in different functional spaces showed no significant effect of music on communication or emotional states during communication. Secondly, the average score of the musical evaluation was 1.09 higher in the warm-toned space compared to the cold-toned space. Thirdly, the differences in the effects of music on emotion during communication in different sound environments were significant and pleasure, arousal, and dominance could be efficiently enhanced by music in the quiet space. Fourthly, dominance was 0.63 higher in the minimally furnished space. Finally, we also investigated influence of social characteristics on the effect of music on communication in different indoor spaces, in terms of the intimacy level, the gender combination, and the group size. For instance, when there are more than two communicators in the dining space, pleasure and arousal can be efficiently enhanced by music. This study shows that combining the sound environment with spatial factors (for example, the visual and sound atmosphere) and the interior furnishings can be an effective design strategy for promoting social interaction in indoor spaces.
... 4,[6][7][8][9][10] The Open Geospatial Consortium (OGC), an international non-profit organization committed to developing quality open standards for the global geospatial community, has also published IndoorGML to describe 3D indoor networks. 11,12 Indoor space, which is composed of various architectural components and can be used to accommodate occupants, is often set as the main building object in these models. 11,13 A floor, room, corridor, staircase or courtyard can be modelled as an indoor space. ...
... 11,12 Indoor space, which is composed of various architectural components and can be used to accommodate occupants, is often set as the main building object in these models. 11,13 A floor, room, corridor, staircase or courtyard can be modelled as an indoor space. Although many indoor network models have been proposed and applied, most studies have focused on indoor navigation applications; 6,8 thus, improved emergency evacuation routing support is still needed. ...
... FED Heat , FED co , FED o2 and VCO2 in equations (10) and (11) are all calculated according to the equations in the SFPE Handbook of Fire Protection Engineering. 20 The exposure time of each grid is G g ðtÞ as defined in equation (7). ...
A safe and effective evacuation route is important for reducing casualties during building fires, and this topic has been a long-term focus of emergency management. Indoor space is a space within one or multiple buildings consisting of architectural components and is the basis of indoor route analyses. Based on indoor space, an indoor network model is designed that considers fire protection design, spatial matching between fire information and the indoor space, and the passability of the indoor space during a fire. Then, an evacuation route selection algorithm that considers hazard and time is proposed. The key features of the route selection algorithm, which include assessing the fire hazards, evacuation time in each indoor space and parallel computing in the route selection algorithm, are subsequently presented. Finally, taking a gymnasium as an example, case modelling and fire information integration are performed to investigate two fire scenarios and the optimal evacuation routes over time are identified in each scenario.
... Rules are supposed to be used for inference. In [40], we propose a model for classifying rooms based on the ontology of indoor spaces, which takes into account both their semantic and geometric characteristics. The model is an extension to the IndoorGML data standard. ...
The proposed technique facilitates time-consuming procedure for setting up Wi-Fi or Bluetooth access points, indoor map building and signal propagation model calibration. The technique based on OWL ontology and the SLAM method includes the phase of forming a training sample, as well as the phase of simultaneous navigation and mapping. The SLAM method implements The Gaussian Process Latent Variable Model (GP-LVM). The proposed method is based on solving the regression problem using machine learning methods to form a training sample, as well as solving the problem of reducing the dimension for simultaneous navigation and map building. As a training sample, the smartphone‘s internal sensor readings (steps and rotation angles) and Wi-Fi received signal strength values obtained using crowd calculations are used. The resulting training sample is used to determine the parameters of the correlation function that sets the correlation between the user‘s localization points. The proposed ontology is intended to determine different events occurring during user’s movement and involve the appropriate phase of the proposed technique.
... The details of available ontologies for reusability are listed in Table 1. [69,70] MIMU-Wear Ontology [71] MES ontology [72][73][74] IndoorGML [75] SmartBAN Ontology [76] ERP ontology [77,78] Navigation ontology [70,79,80] HealthIoT Ontology [81] PLM ontology [73] Indoor space ontology [82,83] Fitbit Ontology [84] Vital Sign Ontology [85] ERP To map the generated data sources in this industrial scenario, three ontologies were chosen for data modeling. The selection criteria were chosen on basis of the degree of matching between the ontological schema and data source structure. ...
Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture.
... Studies of indoor data models focus on conceptualizing, standardizing, and expressing the various spaces constituting the indoor environment and their relationships [4,5,[7][8][9][10][11][12][13], as well as adapting and tailoring existing data models to the needs of application services [6]. Two kinds of models can be created: (1) generalized indoor models for broad classes of users; (2) models for specific purposes. ...
... Two kinds of models can be created: (1) generalized indoor models for broad classes of users; (2) models for specific purposes. Many studies have performed modeling based on indoor space standards; these models include CityGML, IndoorGML, and the spatial information model defined in ISO19107 [5,8,10,[13][14][15][16][17]. ...
... Unlike object-based CityGML, IndoorGML represents indoor spaces as constrained, semantic spaces, and models based on IndoorGML are thus more suitable for expressing the connectivity of various indoor spaces [1,2]. In [8], a space classification model is proposed that considers both the semantic and geometric characteristics of the space in the ontology of indoor space. The effectiveness of this model was demonstrated by designing an IndoorGML extension using proposed classes, including syntactic and semantic components. ...
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.
... The authors propose an original approach, producing smoother and more natural paths than other existing methods. The third paper [8] proposes an ontology of indoor space, incorporating both a geometrical and a semantic component. While existing models like IndoorGMLs can already capture the geometry, the ontology is used to provide a semantic extension to IndoorGMLs allowing for a much more comprehensive description of the indoor environment. ...
The advent of new data collection technologies, such as LiDAR and drones, have made geospatial data available in large amounts and at low costs. While access to data is getting easier, geospatial tools have to evolve towards further automation and guarantee the reproducibility of the process and the quality of the results. As such, algorithms and data structures for handling geospatial data also need to be more and more robust and efficient to model complex, multidimensional geospatial phenomena in GISystems and provide higher levels of analysis. Articles in this special issue address two complementary aspects of the problem. They introduce new algorithms and data structures that allow for a more efficient handling of multidimensional data but also present complete processing chains dealing with the integration and the dissemination of multidimensional data.
One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process to be perform every time when there are changes in equipment configuration and allocation. The developed indoor localization system provides navigation of the user inside a room and allows to building radio map and putting Bluetooth Low Energy (BLE) beacons on the map of a room by the efforts of a number of users walking indoors. The architecture of the system is developed so that the different indoor localization techniques can be used and different services can be requested by the users mobile application. The user's navigation inside the room is a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. The indoor navigation ontology is implemented to make decision which of these methods should be used. The key feature of the system is determining the location of BLE beacon. For this purpose the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. The geometric parameters of a room are estimated by the radio map and Bluetooth beacon locations.
