To spot fire damage from space, point this AI at satellite imagery
A new deep-learning algorithm studies aerial photographs after fires to identify damage.
How it works: From satellite images taken before and after the California wildfires of 2017, researchers created a data set of buildings that were either damaged or left unscathed.
The results: They tweaked a pre-trained ImageNet neural network and got it to spot damaged buildings with an accuracy of up to 85 percent.
Why it matters: After a disaster, pinpointing the hardest-hit areas could save lives and help with relief efforts. The researchers also released the data set to the public, which could improve other research that requires satellite images, like conservation and developmental aid work.
Deep Dive
Artificial intelligence
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Galactica was supposed to help scientists. Instead, it mindlessly spat out biased and incorrect nonsense.
A bot that watched 70,000 hours of Minecraft could unlock AI’s next big thing
Online videos are a vast and untapped source of training data—and OpenAI says it has a new way to use it.
Responsible AI has a burnout problem
Companies say they want ethical AI. But those working in the field say that ambition comes at their expense.
Biotech labs are using AI inspired by DALL-E to invent new drugs
Two groups have announced powerful new generative models that can design new proteins on demand not seen in nature.
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