PSI - Issue 78

Mario Graniero et al. / Procedia Structural Integrity 78 (2026) 1040–1047

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acquisition, processing, and analysis using FW-UAVs to ensure consistency and comparability across different projects and regions. Secondly, continued research and development in AI algorithms specifically tailored for identifying subtle infrastructure damage and predicting seismic responses will further enhance the accuracy and automation of vulnerability assessments. This includes exploring federated learning approaches for distributed data analysis and edge computing for real-time processing directly on the UAV. The future integration of UAV oblique photography technology with AI algorithms and virtual reality (VR) imaging is also expected to further improve the efficiency and accuracy of geological disaster management. Furthermore, integration with existing infrastructure management systems (IMS) and early warning systems will be crucial for seamless data flow and decision-making. Developing robust communication protocols to transmit large datasets rapidly from remote areas will also be essential. Finally, policy and regulatory frameworks need to evolve to support the safe and efficient operation of FW-UAVs for critical infrastructure monitoring, facilitating broader adoption and investment. By addressing these challenges, FW-UAV technology can fulfill its promise as a cornerstone of resilient infrastructure management, drastically reducing downtime and safeguarding communities in the face of natural disasters. References Brauchle, J., Geßner, M., Kraft, T., Hein, D., Lesmeister, M., Gonschorek, J., Bock, M., Berger, R., 2024. Regional Rapid Mapping for First Responders - Turkey 2023 Earthquake, in: Pirasteh S., Yepez-Rincon F.D. (Eds.), Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 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