The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 337–344, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-337-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 337–344, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-337-2020

  25 Aug 2020

25 Aug 2020

TOWARDS INDOORGML 2.0: UPDATES AND CASE STUDY ILLUSTRATIONS

A. A. Diakité1, S. Zlatanova1, A. F. M. Alattas2, and K. J. Li3 A. A. Diakité et al.
  • 1Faculty of Built Environment, University of New South Wales, Kensington, NSW 2052, Australia
  • 2Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands
  • 3Pusan National University, Kumjeong-Gu, Pusan 46241, South Korea

Keywords: IndoorGML, Indoor LBS, Standard, 3D Indoor, Topological Network, Semantic

Abstract. Indoor environments differ from outdoor in many aspects. This, added to the limitations faced by other common standards for urban features reinforced the need of setting a dedicated standard for indoor applications. IndoorGML was born in this context to provide the basic concepts, data models, and standard that meet the requirements of indoor spatial applications. Indoor spatial information can be generally classified into two categories: indoor objects such as architectural components (walls, stairs, slabs) and interior facilities (furniture); indoor spaces such as cavities (rooms and corridors) or virtual subdivision (sensor and legal spaces). Handling both information is necessary to support applications ranging from Indoor location-based services (LBS), indoor route analysis or indoor geo-tagging to building and asset management. In this paper, we present the proposed changes to the second version of IndoorGML, under preparation and intended to provide the necessary support for applications using information from those two categories. IndoorGML 2.0 is open to all applications that rely on indoor spaces and require analysis that can be performed on a network, extracted from those spaces utilizing neighbourhood relationships. It follows a model-driven approach, i.e. all concepts are presented by the Unified Modelling Language, from which technical implementations are derived (GML, JSON, SQL, etc.). We present the proposed changes to the previous version, illustrate a way of representing indoor objects other than spaces and discuss several use cases of the standard.