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This item is available under a Creative Commons License for non-commercial use only



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Presented at IEEE YP Conference on Remote Sensing, Barcelona, 2015.


Geographical Information Scientists have a need to combine data from many sources and in various ways to synthesize new understanding, producing new knowle­dge [1]. Remote sensor deployments, monitoring environmental phenomena, are a huge provider of valuable data. Often, observation systems are built in isolation, and the data representations are not adequately designed for re-use and higher order knowledge generation. There are many standards that allow syntactic interoperability and sharing of remote sensor systems observational data, such as the OGC’s suite of standards [2]. However, semantic interoperability remains a work in progress [3] [4]. This presentation describes how system design techniques used in the health informatics domain [5] to tackle similar problems of how data, information and knowledge concepts are modelled and managed can be applied to remote sensing applications. Much like the health domain, remotely sensed data is traditionally modelled from a computer science perspective. Traditional object-oriented techniques typically used to model complex data are insufficient in a geographical data context, as they are too stringent during the early stages of knowledge acquisition. Standards such as O&M on their own precipitate a codifying effect as systems are developed, constraining rapidly evolving information [6]. The authors have investigated the OGC’s O&M standard as a reference model to underpin a two-level modelling approach. An augmented O&M model has been developed and is presented along with a worked example of how a two-level modelling approach using O&M as the reference model can be applied to modelling a marine data buoy. [1] M. Gahegan and W. Pike, "A situated knowledge representation of geographical information," Transactions in GIS, vol. 10, pp. 727-749, 2006. [2] M. Botts, G. Percivall, C. Reed and J. Davidson, "OGC® sensor web enablement: Overview and high level architecture," in GeoSensor Networks Springer, 2008, pp. 175-190. [3] S. Cox, "An explicit OWL representation of ISO/OGC observations and measurements." in Ssn@ Iswc, 2013, pp. 1-18. [4] A. M. Leadbetter, R. K. Lowry and D. O. Clements, "Putting meaning into NETMAR–the open service network for marine environmental data," International Journal of Digital Earth, pp. 1-18, 2013. [5] T. Beale, "Archetypes: Constraint-based domain models for future-proof information systems," in OOPSLA 2002 Workshop on Behavioural Semantics, 2002. [6] M. F. Goodchild, "GIScience ten years after Ground Truth," Transactions in GIS, vol. 10, pp. 687-692, 2006.