Why drones and AI can’t quickly find missing flood victims, yet

(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.)

(THE CONVERSATION) For search and rescue, AI is not more accurate than humans, but it is far faster.

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Recent successes in applying computer vision and machine learning to drone imagery for rapidly determining building and road damage after hurricanes or shifting wildfire lines suggest that artificial intelligence could be valuable in searching for missing persons after a flood.

Machine learning systems typically take less than one second to scan a high-resolution image from a drone versus one to three minutes for a person. Plus, drones often produce more imagery to view than is humanly possible in the critical first hours of a search when survivors may still be alive.

Unfortunately, today’s AI systems are not up to the task.

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We are robotics reseachers
who study the use of drones in disasters. Our experiences searching for victims of flooding and numerous other events show that current implementations of AI fall short.

However, the technology can play a role in searching for flood victims. The key is AI-human collaboration.

Searching for flood victims is a type of wilderness search and rescue that presents unique challenges. The goal for machine learning scientists is to rank which images have signs of victims and indicate where in those images search-and-rescue personnel should focus. If the responder sees signs of a victim, they pass the GPS location in the image to search teams in the field to check.

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The ranking is done by a classifier, which is an algorithm that learns to identify similar instances of objects – cats, cars, trees – from training data in order to recognize those objects in new images. For example, in a search-and-rescue context, a classifier would spot instances of human activity such as garbage or backpacks to pass to wilderness…

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