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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Autors principals:	Chang Liu, Linlin Ge, Ting Bai
Format:	Artigo
Idioma:	Inglês
Publicat:	Taylor & Francis Group 2024-12-01
Col·lecció:	International Journal of Digital Earth
Matèries:	Post-earthquake Lidar building damage deep learning
Accés en línia:	https://www.tandfonline.com/doi/10.1080/17538947.2024.2441934
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