Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid

Zhang, Lin; Xu, Zhimin; Hu, Dan; Hu, Qinwu; Li, Wenjing

doi:10.3390/ijgi6110343

Cited by 19 publications

(22 citation statements)

References 28 publications

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“…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.…”

Section: Related Workmentioning

confidence: 99%

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

Park

Kim

2020

IJGI

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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.

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Section: Related Workmentioning

confidence: 99%

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

Park

Kim

2020

IJGI

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“…The spatial panel data model was a widely applied tool in both in science and social science fields [44,45]. It was used to calculate and estimate the effects of one social attribute in a zone on the same attributes in the adjacent zone [46].…”

Section: Spatial Panel Data Modelmentioning

confidence: 99%

The Spatial and Temporal Research on the Coupling and Coordinated Relationship between Social Economy and Energy Environment in the Belt and Road Initiatives

et al. 2019

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The Belt and Road Initiative (BRI) was presented by China’s central government to achieve regional economic cooperation under the background of economic globalization. Energy and environmental problems, as by-products of economic success, are fundamental concerns in this grand project. An integrated method that combined the coupling coordinated degree model, Moran’s I, and the spatial panel data model was used in the research zone. Results of the coupling coordinated degree model indicated that the statuses of the social economy and the energy environment of the countries in the research zone improved from 1997 to 2014, with the changes in China and Eastern Europe being particularly significant. From the spatial econometric perspective, the coupling coordinated degrees of the countries in the research zone appeared in the spatial cluster in accordance with Moran’s I. The result of the spatial panel data model indicated that the coupling coordinated degree of one country positively affected those of its neighboring countries.

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“…Indoor building modelling in 3D space is necessary for different applications, hence fast scanning devices are manufactured for creating a point cloud, which is the source to start modelling. Besides, various indoor models, such as geometric, grid, topology, semantic, and hybrid models have been proposed to deal with these data [13]. Despite these different models, 3D modelling from a point cloud is still time-consuming, because of the manual involvement [14].…”

Section: Introductionmentioning

confidence: 99%

Semantic Geometric Modelling of Unstructured Indoor Point Cloud

Shi

Ahmed

et al. 2018

IJGI

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A method capable of automatically reconstructing 3D building models with semantic information from the unstructured 3D point cloud of indoor scenes is presented in this paper. This method has three main steps: 3D segmentation using a new hybrid algorithm, room layout reconstruction, and wall-surface object reconstruction by using an enriched approach. Unlike existing methods, this method aims to detect, cluster, and model complex structures without having prior scanner or trajectory information. In addition, this method enables the accurate detection of wall-surface “defacements”, such as windows, doors, and virtual openings. In addition to the detection of wall-surface apertures, the detection of closed objects, such as doors, is also possible. Hence, for the first time, the whole 3D modelling process of the indoor scene from a backpack laser scanner (BLS) dataset was achieved and is recorded for the first time. This novel method was validated using both synthetic data and real data acquired by a developed BLS system for indoor scenes. Evaluating our approach on synthetic datasets achieved a precision of around 94% and a recall of around 97%, while for BLS datasets our approach achieved a precision of around 95% and a recall of around 89%. The results reveal this novel method to be robust and accurate for 3D indoor modelling.

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Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid

Cited by 19 publications

References 28 publications

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

The Spatial and Temporal Research on the Coupling and Coordinated Relationship between Social Economy and Energy Environment in the Belt and Road Initiatives

Semantic Geometric Modelling of Unstructured Indoor Point Cloud

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