EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

HKUST-Landslide Susceptibility Dataset (HKUST-LSD): A benchmark dataset for landslide susceptibility assessment in Hong Kong

Haojie Wang1, Limin Zhang1,2, and Lin Wang1
Haojie Wang et al.
  • 1Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR.
  • 2HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China.

Rain-induced natural terrain landslides are the most frequent geo-hazard in many regions of the world. As an essential tool in addressing rising landslide challenges due to climate change, landslide susceptibility assessment has been widely investigated in Hong Kong for over twenty years. However, a public dataset for Hong Kong landslide susceptibility assessment is currently absent in the geoscience research community, which brings difficulties in establishing consistent evaluation criteria for testing any new method or theory. Thus, to facilitate the development of new statistical and/or artificial intelligence-based methods for landslides susceptibility assessment, here we compile the first version of The Hong Kong University of Science and Technology – Landslide Susceptibility Dataset (HKUST-LSD) based on multiple sources of open data. Aiming at comprehensively describing the rain-induced natural terrain landslide conditioning factors in Hong Kong, HKUST-LSD v1.0 comprises data of (a) a landslide inventory; (b) a high-resolution digital terrain model (DTM) and its topographical derivatives; (c) superficial geology, distance to faults and rivers/sea; (d) historical maximum rolling rainfall and (e) ground vegetation condition. HKUST-LSD v1.0 provides a ready-to-use dataset that includes processed landslide and non-landslide samples, together with reference codes that utilized representative machine learning techniques to assess the landslide susceptibility in Hong Kong and achieved satisfactory performance. The dataset will be updated on a regular basis to fulfil the latest research needs that might arise in the research community and support global sustainable development.

Download the dataset at:

How to cite: Wang, H., Zhang, L., and Wang, L.: HKUST-Landslide Susceptibility Dataset (HKUST-LSD): A benchmark dataset for landslide susceptibility assessment in Hong Kong, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6259,, 2023.