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Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data

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 c...

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Hoofdauteurs: Chang Liu, Linlin Ge, Ting Bai
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Taylor & Francis Group 2024-12-01
Reeks:International Journal of Digital Earth
Onderwerpen:
Post-earthquake
Lidar
building damage
deep learning
Online toegang:https://www.tandfonline.com/doi/10.1080/17538947.2024.2441934
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