Development of Data Fusion Method Based on Topological Relationships Using IndoorGML Core Module

Abstract

Geospatial datasets are currently constructed, managed, and utilized individually according to the spatial scale of the real world, such as the ground/surface/underground or indoor/outdoor, as well the particular purpose of the geospatial data used for location-based services. In addition, LBS applications use an optimal data model and data format according to their particular purpose, and thus, various datasets exist to represent the same spatial features. Such duplicated geospatial datasets and geographical feature-based GIS data cause serious problems in the financial area, compatibility issues among LBS systems, and data integration problems among the various geospatial datasets generated independently for different systems. We propose a geospatial data fusion model called the topological relation-based data fusion model (TRDFM) using topological relations among spatial objects in order to integrate different geospatial datasets and different data formats. The proposed model is a geospatial data fusion model implemented in a spatial information application and is used to directly provide spatial information-based services without data conversion or exchange of geometric data generated by different data models. The proposed method was developed based on an extension of the AnchorNode concept of IndoorGML. The topological relationships among spatial objects are defined and described based upon the basic concept of IndoorGML. This paper describes the concept of the proposed TRDFM and shows an experimental implementation of the proposed data fusion model using commercial 3D GIS software. Finally, the limitations of this study and areas of future research are summarized.

BIM compatibility and its differentiation with interoperability challenges as an innovation factor

“…They discuss two types of limitations, such as internal and external latency, including delay in data entry, transmission, processing, perception, evaluation, judgement, and response. Park et al [111] also extend the list of limitations when examining logical and geometrical topological relations. They report that heterogenous data formats, models, spatial resolutions and geometric resolution methods are key barriers of an efficient data fusion practice.…”

“…Applications of BIM in electrical systems design [110] Analysis of a case study of lighting design for a small office room inside a substation using an add-in tool to extract lighting information (2017) Issues in automating the extraction of feeder schedule with voltage changes, single line charts, size adjustment of cables and circuit breakers based on approved codes, lack of standards and freeware BIM tools. Data Fusion and IndoorGML core module [111] Using AnchorNode of IndoorGML (2018)…”

Integrating Image and Network-Based Topological Data through Spatial Data Fusion for Indoor Location-Based Services

“…In many cases, multiple datasets may exist to represent the same geographic features existing in the real world [18]. These may represent various aspects of the feature they represent, or each of them is a representation using different data models that are accordingly suited to each application.…”

“…On the other hand, Topological Relation Model (TRM) [20] essentially establishes connections between data through matching geometric data generated from respective models. Since not all datasets may be a source of geometric information, the Topological Relation-based Data Fusion Model approached the problem by generating topological data from surface, network, and volume-based data to estab-lish matching [18]. Commonly, these approaches determine a correspondence between the features in the data as an approach to data fusion.…”

Developing a Data Fusion Strategy Between Omnidirectional Image and Indoorgml Data

“…The first one is mainly used for BIM and CityGML datasets, and this requires identification of corresponding objects between source and target data, making it inapplicable for images. Visualization based approaches however, has limitations on providing seamless indoor and outdoor services Park et al, 2018).…”