Tornadoes can be devastating to communities, causing both structural and financial damage. Assessing these damages can take weeks to months following a tornado — until now.
Led by Dr. Maria Koliou, Texas A&M researchers have developed a new method that combines remote sensing, deep learning and restoration models to speed up building damage assessments after a tornado. Once post-event images are available, the model can produce damage assessments and recovery forecasts in less than an hour.
The model can also predict repair costs and estimate recovery times, helping communities bounce back from these devastating natural hazards.
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.
One of the most interesting findings was that, in addition to detecting damage with high accuracy, we could also estimate the tornado's track. 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.
