Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data
Name
Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data.pdf
Description
Published version
Size
2.57 MB
Format
Adobe PDF
Checksum (MD5)
6603c4e7522f034095bfc60f554a6d8f
Author(s)
Liu, Chang
Ge, Linlin
Bai, Ting
Date Issued
December 31, 2024
Journal
International Journal of Digital Earth
Publisher
Taylor & Francis
Citation
Liu, C., Ge, L., & Bai, T. (2024). Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data. International Journal of Digital Earth, 17(1).
Version
Final published version
Abstract
In post-earthquake scenarios, the swift assessment of building damage levels is pivotal for efficient emergency response and recovery planning. Nevertheless, conventional in-situ damage evaluations consume time. Current satellite-based deep learning methods save time but often lack detail, usually classifying damage as either collapsed or intact. This two-level information is not enough for rescue or recovery planning. Light Detection and Ranging (Lidar)-based deep learning methods, which provide three-dimensional (3D) information, could address this issue of damage details. Therefore, this paper proposes a deep learning-based building damage level classification method using both Lidar and satellite data. The proposed method classifies damage into four levels, including no/minor damage, partially collapsed, totally collapsed, and story failure. The developed network builds upon RandLA-Net, incorporating surface normal vectors to enhance accuracy. A colourised Lidar dataset was created for the network. The network underscores the advantage of incorporating surface normal information. A framework is also proposed based on the damage level outcomes of the developed network, which aids in emergency response efforts. Consequently, this paper demonstrates the practical utility of deep learning networks in rapidly assessing detailed building damage levels after earthquakes. Its practical contribution is guiding decision-making during the critical phases of post-earthquake response and recovery.
MIT Department
Senseable City Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/17538947.2024.2441934
