Structural Damage Detection Using AI Super Resolution and Visual Language Model

TL;DR

This paper introduces an AI-powered framework combining super-resolution video enhancement and visual language models to rapidly and accurately assess structural damage from drone footage in disaster scenarios.

Contribution

It presents a novel integrated system that enhances low-resolution footage and classifies damage, improving disaster assessment efficiency and accessibility for non-technical users.

Findings

Achieved 84.5% damage classification accuracy.

Validated on drone imagery from the 2023 Turkey earthquakes.

Demonstrated effectiveness in resource-limited disaster response settings.

Abstract

Natural disasters pose significant challenges to timely and accurate damage assessment due to their sudden onset and the extensive areas they affect. Traditional assessment methods are often labor-intensive, costly, and hazardous to personnel, making them impractical for rapid response, especially in resource-limited settings. This study proposes a novel, cost-effective framework that leverages aerial drone footage, an advanced AI-based video super-resolution model, Video Restoration Transformer (VRT), and Gemma3:27b, a 27 billion parameter Visual Language Model (VLM). This integrated system is designed to improve low-resolution disaster footage, identify structural damage, and classify buildings into four damage categories, ranging from no/slight damage to total destruction, along with associated risk levels. The methodology was validated using pre- and post-event drone imagery from the 2023 Turkey earthquakes (courtesy of The Guardian) and satellite data from the 2013 Moore Tornado (xBD dataset). The framework achieved a classification accuracy of 84.5%, demonstrating its ability to provide highly accurate results. Furthermore, the system's accessibility allows non-technical users to perform preliminary analyses, thereby improving the responsiveness and efficiency of disaster management efforts.