Volume IV-4 | Copyright
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4, 179-186, 2018
https://doi.org/10.5194/isprs-annals-IV-4-179-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Sep 2018

19 Sep 2018

LINKING INTERACTIVE OPTIMIZATION FOR URBAN PLANNING WITH A SEMANTIC 3D CITY MODEL

N. Schüler1, G. Agugiaro2, S. Cajot1, and F. Maréchal1 N. Schüler et al.
  • 1Industrial Process and Energy Systems Engineering Group, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, 1950 Sion, Switzerland
  • 2Smart and Resilient Cities Unit, Austrian Institute of Technology (AIT), Giefinggasse 4, 1210 Vienna, Austria

Keywords: urban planning, decision support, interactive optimization, data model, CityGML, Scenario ADE, URBio

Abstract. The cities in which we live are constantly evolving. The active management of this evolution is referred to as urban planning. The according development process could go in many directions resulting in a large number of potential future scenarios of a city. The planning support system URBio adopts interactive optimization to assist urban planners in generating and exploring those various scenarios. As a computer-based system it needs to be able to efficiently handle all underlying data of this exploration process, which includes both methodology-specific and context-specific information. This article describes the work carried out to link URBio with a semantic city model. Therefore, two key requirements were identified and implemented: (a) the extension of the CityGML data model to cope with many scenarios by the proposition of the Scenario Application Domain Extension (ADE) and (b) the definition of a data model for interactive optimization. Classes and features of the developed data models are motivated, depicted and explained. Their usability is demonstrated by walking through a typical workflow of URBio and laying out the induced data flows. The article is concluded with stating further potential applications of both the Scenario ADE and the data model for interactive optimization.

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