The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Dec 2022)

MANAGING INDOOR MOVABLE ASSETS IN 3D USING CITYGML FOR SMART CITY APPLICATIONS

  • A. A. M. Nasir,
  • S. Azri,
  • U. Ujang,
  • T. L. Choon

DOI
https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-103-2022
Journal volume & issue
Vol. XLVIII-4-W3-2022
pp. 103 – 110

Abstract

Read online

Smart Cities are the extensive use of the Internet of Things (IoT) and open data to optimise the flow of energy, people, and data, especially in city infrastructure, management, and services. Nowadays, city or building management has become widespread regarding smart city development. Moreover, Asset Management is becoming increasingly important for cities to have the conditions for continuous development, buildings and urban infrastructures that must be planned more efficiently and sustainably. On the other hand, asset management is no longer appropriate since the appearance properties, textures, and materials attached to city models drastically increase the loading time for visualisation and spatial analysis. Besides, different applications or users demand different Level of Details (LODs); hence, a suitable 3D indoor model is needed. However, the available models representing indoor spaces’ geometric and semantic information are rarely described. Thus, to achieve effective asset management, we proposed organising indoor movable assets of indoor building models using CityGML. This paper aims to model the indoor movable assets based on Level of Detail 4 (LOD4) using CityGML concept. There are several limitations of current indoor movable asset management practice, such as the unavailability of asset visualisation, outdated asset information, and a tedious filing system. Therefore, integrating indoor asset management and the CityGML seems to be a better option to manage the 3D asset management information efficiently. This study uses the 3D building model in level 4 (LOD4) to conceptualise and organise the indoor asset information and visualisation. The final output of this study is indoor asset information in LOD 4. Each of the components can be viewed individually together with its information. For future use, the model can be used to serve various smart city applications such as indoor navigation, design, and planning.