Abstract
This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data.
Highlights
The 3D geospatial data domain with its derived 3D geospatial information and applications is experiencing rapid development in all its aspects, including 3D spatial data acquisition, spatial data modelling and processing, spatial analysis and visualization [1,2,3,4]. 3D geospatial data and 3D geoinformation applications have gained momentum in the past decade as a rapidly developing geospatial industry with new sensors and platforms, as well as the rapid progress of information and communication technology, opened up possibilities for acquiring, collecting, modelling and analysing big geospatial datasets
The main aim of this study is to develop a methodology to perform the extraction of an indoor navigation network based solely on 3D geometry using SFCGAL functions, and to discuss the issues and advantages in using this approach
It is possible to apply the proposed methods to any other 3D cadastral dataset if the data format is supported by the FME and if it follows the same data modelling principle, having 3D geometries of indoor spaces in ItSoPuRcShIinnt.gJ.rGeeloa-tIinof.n2s0h20i,p9s,.41T7he approach for modelling such datasets from existing 2D documenta7toiof n15, which can be found in many countries worldwide, has been proposed in [24]
Summary
The 3D geospatial data domain with its derived 3D geospatial information and applications is experiencing rapid development in all its aspects, including 3D spatial data acquisition, spatial data modelling and processing, spatial analysis and visualization [1,2,3,4]. 3D geospatial data and 3D geoinformation applications have gained momentum in the past decade as a rapidly developing geospatial industry with new sensors and platforms, as well as the rapid progress of information and communication technology, opened up possibilities for acquiring, collecting, modelling and analysing big geospatial datasets. The 3D geospatial data domain with its derived 3D geospatial information and applications is experiencing rapid development in all its aspects, including 3D spatial data acquisition, spatial data modelling and processing, spatial analysis and visualization [1,2,3,4]. The importance of 3D spatial data integration and management lies in the fact that many spatial analyses that produce valuable information use data from various sources, which need to be harmonized and integrated. The demand for and importance of indoor spatial information are rapidly growing, creating a need to integrate the data from different domains [2,12,13]. The extraction and modelling of data and information relevant for indoor navigation from various data sources has been intensively studied in recent years [14,15,16,17,18]. The methodology is applied to two different indoor spatial data sources, i. e. 3D cadastre data and BIM data, which are well-known 3D geometric and attribute data sources for building interiors
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