Spatio-Semantic Comparison of Large 3d City Models in Citygml Using a Graph Database

Abstract

ABSTRACT:A city may have multiple CityGML documents recorded at different times or surveyed by different users. To analyse the city's evolution over a given period of time, as well as to update or edit the city model without negating modifications made by other users, it is of utmost importance to first compare, detect and locate spatio-semantic changes between CityGML datasets. This is however difficult due to the fact that CityGML elements belong to a complex hierarchical structure containing multi-level deep associations, which can basically be considered as a graph. Moreover, CityGML allows multiple syntactic ways to define an object leading to syntactic ambiguities in the exchange format. Furthermore, CityGML is capable of including not only 3D urban objects' graphical appearances but also their semantic properties. Since to date, no known algorithm is capable of detecting spatio-semantic changes in CityGML documents, a frequent approach is to replace the older models completely with the newer ones, which not only costs computational resources, but also loses track of collaborative and chronological changes. Thus, this research proposes an approach capable of comparing two arbitrarily large-sized CityGML documents on both semantic and geometric level. Detected deviations are then attached to their respective sources and can easily be retrieved on demand. As a result, updating a 3D city model using this approach is much more efficient as only real changes are committed. To achieve this, the research employs a graph database as the main data structure for storing and processing CityGML datasets in three major steps: mapping, matching and updating. The mapping process transforms input CityGML documents into respective graph representations. The matching process compares these graphs and attaches edit operations on the fly. Found changes can then be executed using the Web Feature Service (WFS), the standard interface for updating geographical features across the web.

Software Systems Approach to Multi-Scale GIS-BIM Utility Infrastructure Network Integration and Resource Flow Simulation

“…A graph database was selected as they have been found to be efficient for storing and querying topologically connected data [24,25]. Furthermore, it has been proposed that graph theory and graph models are suitable for understanding urban topologies [26] and integrating models of urban data [27], and that graph databases can be used for the detection of spatial-semantic changes in CityGML documents [28]. The sub-networks derived from each data source are integrated into a single network by executing Neo4j Cypher queries that run 'merge' clauses on building nodes with matching identifiers across the CityGML and IFC files, and JSON stream.…”

An IOT architecture for facilitating integration of geoinformation

“…As many IoT architectures would have a need to store many relationships between the nodes, the second objective of the study was focused on testing the applicability of the graph databases in an IoT Architecture. Previous studies indicating the potential and uses of Graph Databases for GeoInformation storage and retrieval include Agoub et al (2016) and Nguyen et al(2017).As the data in a graph database is more connected than standard SQL database, the query response times of graph databases are much faster, especially when there is a lot of relationships between objects. In parallel with these aim and objectives of the research, the user requirements are identified and unified in a generic high-level single use-case scenario.…”

Semantic Data Management for a Virtual Factory Collaborative Environment

“…For instance, in [35] the authors solely target the issue of finding the hyponyms in a specific ontology, which limits the approach to a specific ontology containing specific relation types. The issue of importing ontologies is raised in several works (e.g., [29,31,37]), whereas in [38], the authors propose to use a graph database as a "hub" to aggregate the data for further analysis.…”

“…Some specific application areas (e.g., utilization of graph database as advisor for academic collaboration [34], mapping documents containing the smart city information into the graph nodes [37], or how ontologies mapping can contribute to the cross standard interoperability in automation [39]) are further considered in the literature. Though, our aim is to utilize the framework presented in this work for multiple domains.…”