Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

This study aims to improve the interoperability between the application domains of Building Information Modelling (BIM) and Geographic Information Systems (GIS) by linking and harmonizing core information concepts. Many studies have investigated the integration of application schemas and data instances according to the BIM model IFC and the GIS model CityGML. This study investigates integration between core abstract concepts from IFC and ISO/TC 211 standards for GIS—independent of specific application schemas. A pattern was developed for conversion from IFC EXPRESS schemas to Unified Modelling Language (UML) models according to ISO/TC 211 standards. Core concepts from the two application domains were linked in the UML model, and conversions to implementation schemas for the Geography Markup Language (GML) and EXPRESS were tested. The results showed that the IFC model could be described as an ISO/TC 211 compliant UML model and that abstract concepts from ISO/TC 211 standards could be linked to core IFC concepts. Implementation schemas for BIM and GIS formats could be derived from the UML model, enabling implementation in applications from both domains without conversion of concepts. Future work should include refined linking and harmonization of core abstract concepts from the two application domains.

This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios.