Presentation Information
[eHvadpaV-04]Semantic Space Modeling of Ocean Environments Using a CityGML-Based Approach
*Seong Gon Kim1 (1. Sfractum Co., Ltd. (Korea))
This study proposes OceanGML, a semantic modeling framework for digital twins that integrates CityGML 3.0 with IHO S-100 standards. Unlike terrestrial modeling focused on discrete objects, marine environments demand a space-centric paradigm to represent dynamic, vertically stratified fluid domains. To overcome the limitations of static models, OceanGML implements a 4D (x, y, z, t) grid-based structure, enabling the seamless ingestion and synchronization of time-varying physical fields such as ocean currents, temperature, and salinity profiles.
The framework incorporates specialized modules—OceanTopography, OceanLinkBoundary, and OceanObjectGroup—that align with S-100 product specifications (S-101, S-102, S-104). By leveraging the S-100 Part 8 framework, the model facilitates the processing of high-resolution spatiotemporal data, allowing for the dynamic visualization and analytical query of volumetric physical properties. This semantic layering enables multi-stakeholder interpretations of the same ocean volume, from navigation corridors to complex ecological reserves. By effectively bridging the gap between raw geophysical sensor data and semantic spatial models, OceanGML provides a robust, standards-based foundation for advanced analytical ocean digital twins.
The framework incorporates specialized modules—OceanTopography, OceanLinkBoundary, and OceanObjectGroup—that align with S-100 product specifications (S-101, S-102, S-104). By leveraging the S-100 Part 8 framework, the model facilitates the processing of high-resolution spatiotemporal data, allowing for the dynamic visualization and analytical query of volumetric physical properties. This semantic layering enables multi-stakeholder interpretations of the same ocean volume, from navigation corridors to complex ecological reserves. By effectively bridging the gap between raw geophysical sensor data and semantic spatial models, OceanGML provides a robust, standards-based foundation for advanced analytical ocean digital twins.
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