Ambient Spatial Intelligence for Sustainable Cities

Ref 160 1 AMBIENT SPATIAL INTELLIGENCE FOR SUSTAINABLE CITIES Matt DUCKHAM Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Rohan BENNETT Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Ian BISHOP Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Clive Fraser Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Allison Kealy Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Joe Leach Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Cliff Ogleby Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Abbas Rajabifard Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Ian Williamson Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Stephan Winter Department of Geomatics The University of Melbourne Parkville, Victoria 3010, Australia E-mail: [email protected] Abstract: Emerging wireless sensor network (WSN) technologies are opening up many important new opportunities for applications to the built environment, like traffic management, emergency response, and pollution monitoring. However, today’s WSNs have little or no spatial capabilities, and are primarily treated as simply a new data source for integration with other conventional spatial information systems. In contrast, this paper explores the potential for augmenting such networks with the capabilities to not only capture, but also process, query, and even use spatial data in the network itself. The objective is to embed spatial intelligence within the built environment using wireless sensor networks. This “ambient spatial intelligence” (AmSI) presents new fundamental research challenges to many established areas of geomatics and spatial information science. This paper identifies key research problems across geomatics, in the domains of positioning, geographic information science, spatial cognition and geovisualization, spatial data infrastructures, and integration with established data sources like remote sensing and photogrammetry. Using the example of monitoring native vegetation change, the paper indicates how each of these topics is required to contribute to a complete AmSI application to sustainable cities. Emerging wireless sensor network (WSN) technologies are opening up many important new opportunities for applications to the built environment, like traffic management, emergency response, and pollution monitoring. However, today’s WSNs have little or no spatial capabilities, and are primarily treated as simply a new data source for integration with other conventional spatial information systems. In contrast, this paper explores the potential for augmenting such networks with the capabilities to not only capture, but also process, query, and even use spatial data in the network itself. The objective is to embed spatial intelligence within the built environment using wireless sensor networks. This “ambient spatial intelligence” (AmSI) presents new fundamental research challenges to many established areas of geomatics and spatial information science. This paper identifies key research problems across geomatics, in the domains of positioning, geographic information science, spatial cognition and geovisualization, spatial data infrastructures, and integration with established data sources like remote sensing and photogrammetry. Using the example of monitoring native vegetation change, the paper indicates how each of these topics is required to contribute to a complete AmSI application to sustainable cities.