Proceedings of the
8th International Symposium on Geotechnical Safety and Risk (ISGSR)
14 – 16 December 2022, Newcastle, Australia
Editors: Jinsong Huang, D.V. Griffiths, Shui-Hua Jiang, Anna Giacomini, Richard Kelly
doi:10.3850/978-981-18-5182-7_11-015-cd
Automatic Landslide Inventory Generation Using Deep Learning
Department of Civil and Environment Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
ABSTRACT
With the rapid development of deep learning algorithms and easier access to remote sensing images, deep learning-based landslide identification using remote sensing images becomes possible. Pan-sharpening techniques are often adopted to fuse low-resolution multispectral images and high-resolution panchromatic images. This paper combines the deep learning and pan-sharpening techniques to enhance landslide identification results and compares the performance of four pan-sharpening techniques and two deep learning models. Eventually, morphological image processing is adopted to segment landslide clusters into individual landslides and form a basic landslide inventory. A case study of East Sai Kung, Hong Kong, shows that pan-sharpening techniques improve landslide identification accuracy and U-Net model with Brovey sharpening perform the best in this study.
Keywords: Landslide inventory, landslide identification, remote sensing, pan-sharpening, deep learning.
