The Florida Flood and Fire Detection Project: A Machine Learning-Powered Tool to Support Emergency Response in the U.S. After Hurricane Ian
Hurricane Ian left a trail of destruction in Florida, which included high winds and flooding. But a week after the storm passed, some people in three of the worst-hit counties saw an unexpected beacon of hope.
GiveDirectly has previously used the same technology in Togo, but this is the first time it has been used in the US. There, households were offered aid based on signs of poverty detected by image algorithms from researchers at UC Berkeley, and clues from cell phone bills.
The Florida project was powered by a mapping tool called Delphi developed by four machine- learning experts who worked with GiveDirectly over six months. The software shows the need for help after disasters by showing live maps of storm damage and data on poverty from Centers for Disease Control and Prevention. The storm damage data is provided by another Google tool, called Skai, that uses machine learning to analyze satellite imagery from before and after a disaster and estimate the severity of damage to buildings.
The algorithms that power Skai’s damage assessments are trained by manually labeling satellite images of a couple of hundred buildings in a disaster-struck area that are known to have been damaged. The software can then, at speed, detect damaged buildings across the whole affected area. A research paper on the underlying technology presented at a 2020 academic workshop on AI for disaster response claimed the auto-generated damage assessments match those of human experts with between 85 and 98 percent accuracy.
Half of the world lacks adequate early warning systems for disasters like floods and fires, a United Nations report found last month. That’s a lack of lifesaving technology that can give people enough lead time to get themselves to safety. Climate change makes flooding and fires even more dangerous than they were before and the hope is that Google can fill in some of the gaps.
The alerts go out to Android phone users and any phone with the Google search app installed. Companies and organizations can sign up to receive the service if they have access to the internet, but people need to use their phone to receive the flood alert.
If you click on a pin, a box pops up with more information on each location. It will show a range of dates that are past, present, and forward looking. Blue shading in the box denotes whether water levels have already or are expected to surpass a “warning level” and “danger level” over that period of time. More simply, a little arrow next to “River change forecast” at the top of the box indicates whether water levels are expected to rise (with an arrow pointing up) or fall (arrow pointing down).
Comment on “Accurate and Accurate Coverage of Deep-Inelastic Scattering by Electrons and Neutrons” by A.N. Nevo
It is a very big change for us. Nevo says that it is a very meaningfully different system. From a user perspective, they can expect the coverage to be more accurate.