Research
Brief summaries of some of the Lab’s recent research can be found below. Feel free to contact the team if you’d like any more information on any of our research.
The Victorian Government has developed a suite of urban Priority Precincts such as Fishermans Bend. The FILTER project is pioneering a new framework for evaluating outcomes in precincts.
The approach innovates by combining robust, place-based evaluation with quantitative metrics, data-driven benchmarks, and digital tools. FILTER is designed to apply to a range of current and future precincts, with a demonstrator to be developed for Australia’s largest urban renewal project, Fishermans Bend in Victoria.
The FILTER project is a collaboration between RMIT University, including Geographic Knowledge Lab researchers, Victorian Government Department of Transport and Planning (DTP), and the iMove CRC.
The project outcomes will have implications for understanding progress in precincts statewide, nationally, and internationally.
Dynamic Vicmap
The increasing variety and volume of spatial data streams present major challenges to the maintenance of foundation spatial data.
The Dynamic Vicmap project, a collaboration between RMIT University researchers, the SmartSat CRC, Victorian Government, and FrontierSI, proposed a fundamental shift towards a semantic, knowledge-based approach to the management of foundation spatial data updates.
The team developed a proof-of-concept demonstrator that can automate the process of semantic enrichment, integration, interoperability, and dynamic updates of spatial data. The demonstrator was tested with statewide authoritative data and new data streams, including Vicmap hydro, Vicmap property, and machine-learned flooding data.
The results demonstrate the potential for the approach to assist in easier integration of multiple data sets and layers, associated provenance and metadata, and smarter, simpler queries for dynamic foundational spatial data, in Victoria and internationally.
Chat to your data
Chat with your data combines tried-and-tested geospatial queries and spatial analytics with new generative AI technologies, using a technique called “orchestration”.
The approach shows huge promise for making automated access to authoritative spatial data and trustworthy spatial tools much simpler and easier for all. Leveraging the text processing strengths of generative AI together with authoritative spatial data and analytics holds the potential for anyone–not only spatial data analysts and hashtag#GIS technicians–to easily and rapidly access much more of our authoritative spatial data for everyday decisions.
Demonstrating the power of this approach, the research team built an AI chat interface to CLUE, the authoritative Census of Land Use and Employment, made available publicly by the City of Melbourne.
NEXUS
NEXUS addresses the problem of how to make sense of the deluge of physics-based and human sensor data for more timely and accurate intelligence.
The project developed a real-time, streaming geospatial intelligence analytics system capable of support a very wide range of queries, with a particular focus on movement analytics. The system is compatible with standard reference system architectures in defence and industry. Building on this foundations offers the potential to support a widening range of more sophisticated queries, including causal reasoning about the intentions of moving objects.
More information on the NEXUS project appears in publications including:
- Duckham, M., Gabela, J., Kealy, A., et al. (2022). Explainable spatiotemporal reasoning for geospatial intelligence applications. Transactions in GIS, 26, 2455–2479. https://doi.org/10.1111/tgis.12939
- Duckham, M., Gabela, J., Kealy, A., et al. (2023). Qualitative spatial reasoning with uncertain evidence using Markov logic networks. International Journal of Geographical Information Science, 37(9), 2067–2100. https://doi.org/10.1080/13658816.2023.2231044