A machine learning model identified areas likely to have been damaged by Hurricane Leo as it swept through the southern United States.
What’s new: University of Connecticut researchers Zhe Zhu and Su Ye used a learning algorithm to examine satellite images of the storm’s path and spot changes that might indicate wreckage.
How it works: The system was originally designed to identify damage to forests caused by fires, disease, drought, and the like. Given a satellite image, it evaluated changes in real time.
- The authors started with images taken by satellites operated by the United States National Aeronautics and Space Administration and the European Space Agency. They used non-learning algorithms to filter out clouds, snow, and shadows.
- They computed the initial features of each pixel (a vector based on its light spectrum, each representing 30 square meters) based on a time series of 18 prior observations.
- They used a Kalman filter to update a linear model that estimated the changes in each pixel’s vector over time. Given a new observation, if the difference between the estimated and observed vector was great enough, they classified it as a disturbance. If not, they updated the model using the Kalman filter and the current observation.
- They also calculated a disturbance probability, which increased if the changes persisted over repeated observations.
Results: The authors displayed the system’s output as an overlay of yellow squares on a satellite image. Those areas track Ian’s course up the peninsula. They didn’t confirm the damage, however.
Behind the news: Similar approaches to detecting changes in satellite images have been used to assist relief efforts following a number of recent disasters. Researchers have used AI to map surviving roads that relief groups could use to reach victims, direct firefighters towards the most active areas of a woodland blaze, and scan satellite images for signs of impending volcanic eruption.
Why it matters: Satellite imagery can be a boon to responders after a disaster, but the data is often too immense for manual evaluation. AI can enable relief workers to arrive faster and work more effectively. And it’s likely that humanity will need the extra help: Natural disasters such as hurricanes, wildfires, and floods are growing more destructive as global temperatures rise.
We’re thinking: We enthusiastically support the use of AI to guide relief efforts after disasters. We urge agencies that are charged with responding to integrate the technology with their plans.