CityGML urban model generation using national public datasets for flood damage simulations: A case study in Korea.

Managing information at city level has become increasingly important owing to the introduction of smart cities and the increasing severity of disasters due to climate change. A data collection framework, model construction, and information management must be established to systematically manage information at the city level. This study developed an urban model generation method using detailed attributes within the City Geography Markup Language (CityGML), a standard data schema for 3D representation of cities based on different types of publicly available information within Korea. The generated model was used to develop a method for simulating flooding status, degree of flooding, and level of building damage after heavy rainfall, in Korea. Furthermore, we developed a method to estimate the loss of human life and property damage by combining the results of the flood analysis with the city model. The proposed methodology supports the creation of standard-based models for flood analysis and exhibits strong interoperability for application to different areas of analysis.