Geospatial Data Management Research: Progress and Future Directions Without geospatial data management, todayxe2x80x99s challenges in big data applications such as earth observation, geographic information systembuilding information modeling (GISBIM) integration, and 3D4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIMGIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geodata science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the georelated sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Datastreaming libraries and xe2x80x9cinsituxe2x80x9d geocomputing on objects executed directly on the sensors will revolutionize geoinformation science and bridge geocomputing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objectsxe2x80x99 trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.

Towards Integrating Heterogeneous Data: A Spatial DBMS Solution from a CRCLCL Project in Australia Over recent decades, more and more cities worldwide have created semantic 3D city models of their built environments based on standards across multiple domains. 3D city models, which are often employed for a large range of tasks, go far beyond pure visualization. Due to different spatial scale requirements for planning and managing various built environments, integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) has emerged in recent years. Focus is now shifting to Precinct Information Modeling (PIM) which is in a more general sense to builtenvironment modeling. As scales change so do options to perform information modeling for different applications. How to implement data interoperability across these digital representations, therefore, becomes an emerging challenge. Moreover, with the growth of multisource heterogeneous data consisting of semantic and varying 2D3D spatial representations, data management becomes feasible for facilitating the development and deployment of PIM applications. How to use heterogeneous data in an integrating manner to further express PIM is an open and comprehensive topic. In this paper, we develop a semantic PIM based on multisource heterogeneous data. Then, we tackle spatial data management problems in a Spatial Database Management System (SDBMS) solution for our defined unified model. Case studies on the University of New South Wales (UNSW) campus demonstrate the efficiency of our solution.