Skip to Content
Artificial intelligence

To spot fire damage from space, point this AI at satellite imagery

March 8, 2018

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

Geoffrey Hinton tells us why he’s now scared of the tech he helped build

“I have suddenly switched my views on whether these things are going to be more intelligent than us.”

ChatGPT is going to change education, not destroy it

The narrative around cheating students doesn’t tell the whole story. Meet the teachers who think generative AI could actually make learning better.

Deep learning pioneer Geoffrey Hinton has quit Google

Hinton will be speaking at EmTech Digital on Wednesday.

We are hurtling toward a glitchy, spammy, scammy, AI-powered internet

Large language models are full of security vulnerabilities, yet they’re being embedded into tech products on a vast scale.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.