Hurricane Ian left an extraordinarily broad path of
destruction across much of South Florida. That was
evident in reports from the ground, but it also shows
up in satellite data. Using a new method, our team of
spatial and environmental analysts was able to quickly
provide a rare big picture view of damage across the entire state.
By using satellite images from before the storm and real-time
images from four satellite sensors, together with artificial
intelligence, we created a disaster monitoring system that
can map damage in 30-meter resolution and continuously update
It’s a snapshot of what faster, more targeted disaster monitoring
can look like in the future – and something that could eventually
be deployed nationwide.
How artificial intelligence spots the damage
Satellites are already used to identify high-risk areas
for floods, wildfires, landslides and other disasters,
and to pinpoint the damage after these disasters. But most
satelite-based disaster management approaches rely on
visually assessing the latest images, one neighborhood at a time.
Our technique automatically compares pre-storm images with
current satellite images to spot anomalies quickly over large
areas. Those anomalies might be sand or water where that
sand or water shouldn’t be, or heavily damaged roofs that
don’t match their pre-storm appearance. Each area with a
significant anomaly is flagged in yellow.
Five days after Ian lashed Florida, the map showed yellow
alert polygons all over South Florida. We found that it could
spot patches of damage with about 84% accuracy.
A natural disaster like a hurricane or tornado often leaves
behind large areas of spectral change at the surface, meaning
changes in how light reflects off whatever is there, such as
houses, ground or water. Our algorithm compares the reflectance
in models based on pre-storm images with reflectance after the storm.
The system spots both changes in physical properties of natural
areas, such as changes in wetness or brightness, and the overall
intensity of the change. An increase in brightness often is
related to exposed sand or bare land due to hurricane damage.
Using a machine-learning model, we can use those images to predict
disturbance probabilities, which measures the influences of
natural disaster on land surfaces. This approach allows us to
automate disaster mapping and provide full coverage of an entire
state as soon as the satellite data is released.
The system uses data from four satellites, Landsat 8 and Landsat
9, both operated by NASA and the U.S. Geological Survey, and
Sentinel 2A and Sentinel 2B, launched as part of the European
Commission’s Copernicus program.
Real-time monitoring, nationwide
Extreme storms with destructive flooding have been
documented with increasing frequency over large parts of
the globe in recent years.
While disaster response teams can rely on airplane surveillance
and drones to pinpoint damage in small areas, it’s much harder
to see the big picture in a widespread disaster like hurricanes
and other tropical cyclones, and time is of the essence. Our
system provides a fast approach using free government-produced
images to see the big picture. One current drawback is the
timing of those images, which often aren’t released publicly
until a few days after the disaster.
We are now working on developing near real-time monitoring of
the whole conterminous United States to quickly provide the
most up-to-date land information for the next natural disaster.
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System