An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web

ABSTRACT

The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users.

KEYWORDS:

• Geospatial data integration
• geospatial data enrichment
• geospatial semantic web
• urban buildings
• multiple scales

Acknowledgments

The authors would like to thank the Istanbul Metropolitan Municipality and General Directorate of Mapping for providing data.

Data availability statement

The data that supports the findings of this study is openly available in GitHub at https://github.com/carto-web/building-integration.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.