Researchers at Texas A&M University have developed a new method of assessing damage after a disaster that can produce damage assessments and recovery forecasts in less than an hour.

Once post-event images are available, AI-driven remote sensing, deep learning, and restoration models speed up building damage assessments and recovery time predictions after a tornado.

Traditional methods of assessing damage after a disaster can take weeks or even months, delaying emergency response, insurance claims, and long-term rebuilding efforts.

Led by Dr. Maria Koliou, associate professor and Zachry Career Development Professor II in the Zachry Department of Civil and Environmental Engineering at Texas A&M, the researchers published their model in Sustainable Cities and Society.

“Manual field inspections are labor-intensive and time-consuming, often delaying critical response efforts,” said Abdullah Braik, coauthor and a civil engineering doctoral student at Texas A&M. “Our method uses high-resolution sensing imagery and deep learning algorithms to generate damage assessments within hours, immediately providing first responders and policymakers with actionable intelligence.”

The model does more than assess damage, it also helps predict repair costs and estimate recovery times, researchers said.

“We aim to provide decision-makers with near-instantaneous damage assessment and probabilistic recovery forecasts, ensuring that resources are allocated efficiently and equitably, particularly for the most vulnerable communities,” Braik said. “This enables proactive decision-making in the aftermath of a disaster.”

Researchers combined three tools to create the model: remote sensing, deep learning and restoration modeling.

Remote sensing uses high-resolution satellite or aerial images from sources such as NOAA to show the extent of damage across large areas.

“These images are crucial because they offer a macro-scale view of the affected area, allowing for rapid, large-scale damage detection,” Braik said.

Deep learning automatically analyzes the images to identify the severity of the damage. The AI is trained before disasters by analyzing thousands of images of past events, learning to recognize visible signs of damage such as collapsed roofs, missing walls, and scattered debris. The model then classifies each building into categories such as no damage, moderate damage, major damage, or destroyed.

Restoration modeling uses past recovery data, building and infrastructure details, and community factors like income levels or access to resources to estimate how long it might take for homes and neighborhoods to recover under different funding or policy conditions.

When combined, the model can quickly assess the damage and predict short- and long-term recovery timelines for communities affected by disasters, the researchers said.

“Ultimately, this research bridges the gap between rapid disaster assessment and strategic long-term recovery planning, offering a risk-informed yet practical framework for enhancing post-tornado resilience,” Braik said.

Koliou and Braik used data from the 2011 Joplin tornado to test their model due to its massive size, intensity, and availability of high-quality post-disaster information. The tornado destroyed thousands of buildings, creating a diverse dataset that allowed the model to be trained and tested across various levels of structural damage. Detailed ground-level damage assessments provided a reliable benchmark to check how accurately the model could classify the severity of the damage.

“One of the most interesting findings was that, in addition to detecting damage with high accuracy, we could also estimate the tornado’s track,” Braik said. “By analyzing the damage data, we could reconstruct the tornado’s path, which closely matched the historical records, offering valuable information about the event itself.”

Researchers are working on using this model for other types of disasters, such as hurricanes and earthquakes, as long as satellites can detect damage patterns.

“The key to the model’s generalizability lies in training it to use past images from specific hazards, allowing it to learn the unique damage patterns associated with each event,” Braik said. “We have already tested the model on hurricane data, and the results have shown promising potential for adapting to other hazards.”

The research team believes their model could be critical in future disaster response, helping communities recover faster and more efficiently. The team wants to extend the model beyond damage assessment to include real-time updates on recovery progress and tracking recovery over time.

“This will allow for more dynamic and informed decision-making as communities rebuild,” he said. “We aim to create a reliable tool that enhances disaster management efficiency and supports quicker recovery efforts.”

The technology could transform how emergency officials, insurers, and policymakers respond in the crucial hours and days after a storm by delivering near-instant assessments and recovery projections.

Funding for this research was provided by the National Science Foundation.

Original article written by Alyson Chapman, Texas A&M University. (2025, May 14). Tech meets tornado recovery. ScienceDaily. Retrieved May 15, 2025 from www.sciencedaily.com/releases/2025/05/250514175419.htm