City Research Online

Abstract

This work investigates the application of remote sensing technologies within the specific operational context of emergency urban search and rescue (USAR) efforts post-disaster. We thoroughly investigate two innovative methodologies, each tailored to meet distinct operational goals in a USAR setting. The first employs a belief propagation framework that is designed to provide prompt and robust initial damage assessments using sparse data, with the capability to incorporate additional on-site information as it becomes available. The second methodology introduces a modified graph convolutional network to quantify the uncertainty levels inherent in damage classification tasks. Three case studies were considered, using damage assessment data from the 2020 Beirut explosion, the 2021 Haiti earthquake and the 2023 Türkiye-Syria earthquake. Our experimental results demonstrate the potential of these approaches to achieve operational objectives, particularly in terms of robustness and scalability in disaster scenarios. The versatility offered by graph-based methodologies establishes a solid foundation for addressing these dynamic challenges, suggesting a promising direction for continued research in this field.

Publication Type:	Article
Additional Information:	© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Publisher Keywords:	Disaster management, Post-disaster, Urban Search and Rescue (USAR), Remote sensing, Graph-based data analysis, Machine learning
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction
Departments:	School of Science & Technology School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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