Title:
From Chaos to Insight: A Multimodal Framework for Post-Earthquake Reconnaissance Using Online Data
Author(s):
Khalid Mosalam
Publication:
Web Session
Volume:
ws_S6_KhalidMosalam2.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
3/29/2026
Abstract:
In the aftermath of major earthquakes, conducting reconnaissance efforts rapidly is essential for understanding the performance of the built environment and supporting informed decision-making. Meanwhile, virtual reconnaissance based on online data is increasingly gaining attention due to its feature of operating without on-site investigation. This study proposes a multimodal framework to leverage open-source online data to automatically generate structured post-earthquake reconnaissance briefings. The system design is informed by the existing virtual reconnaissance workflow of the Structural Extreme Events Reconnaissance (StEER) network, with key steps identified and automated. The framework integrates multiple APIs and AI components to monitor seismic events globally, scrape and process fragmented, noisy multimodal data from online sources, and extract and summarize valuable information. This work demonstrates the feasibility of using open-source online data and AI for automated virtual reconnaissance and highlights key challenges in developing a domain-specific, AI-assisted reporting framework.