Hong Kong has been grappling with the enduring issue of land scarcity for a long time, while the topography of our city is generally hilly with the limited availability of flat land. To tackle the challenge of boosting the land supply, the Hong Kong Government has carried out studies to explore opportunities for enhancing rock cavern development to increase the land supply of our city. 

Dr. Winnie Tang, Adjunct Professor in the Faculty of Engineering (Department of Computer Science), the Faculty of Social Sciences (Department of Geography) and the Faculty of Architecture at the University of Hong Kong, indicated that, with the help of advanced technologies such as artificial intelligence (AI) and GIS, we will be able to effectively promote the sustainable development of rock caverns.

Geologically, the mountain areas on the edge of the urban areas in Hong Kong “are actually quite suitable for the development of rock caverns.” Dr. Tang explained during the interview of the magazine Vision, published by the Chinese General Chamber of Commerce. The locally unwanted public facilities can be relocated inside the rock cavern in exchange for space to drive sustainable socioeconomic development that enhances the livability of citizens. However, Dr. Tang argued that the construction cost for rock caverns development is currently several times higher than those projects above ground, due to the lack of 3D underground environmental maps and models.  

According to the Cavern Master Plan released by the Civil Engineering and Development Department in conjunction with the Planning Department, there are 48 Strategic Cavern Areas (orange polygon) that are suitable for cavern development. The Cavern Master Plan in geospatial data format is available on the Common Spatial Data Infrastructure Portal. 

To tackle the geotechnical challenges, Dr. Tang suggested to using a smart monitoring system with mixed reality technologies to improve the quality and efficiency of the projects. In addition, 3D underground and cavern environment maps and models can contribute to the establishment of more accurate prediction models, which can greatly reduce risks during planning and development.

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