Teen Develops Flood-Detecting CubeSat – IEEE Spectrum

Currently satellites, synthetic aperture radar, and GPS are used to collect data on flood damage, track the location of victims, and communicate with emergency responders. But technology failures and slow data transmission speeds lead to delays in response time, Merchant says. The increase in global flooding has intensified the need for more accurate and reliable methods.

Last year Merchant built what she says is a more effective way to track and collect data during floods: a small, inexpensive, standardized CubeSat integrated with artificial intelligence. The little satellites use a multiple of 10- by 10- by 10-centimeter units—which allows manufacturers to develop their batteries, solar panels, computers, and other parts as off-the-shelf components.

The CubeSat takes images of an area and uses pattern recognition to detect flooding, assess infrastructure damage, and track survivors.

Merchant presented her paper on the device at this year’s IEEE Region 3 annual conference, IEEE SoutheastCon.

“IEEE is a foundational part of my growth as a young researcher,” she says. “It turned engineering from my dream to reality.”

Building a CubeSat at MIT

Merchant says her interest in disaster response was sparked after learning that it can take several hours for emergency workers to receive satellite data.

Determined to find a faster method, she began researching technologies and discovered what CubeSats can do.

“CubeSats are very agile, scalable, and capable of forming constellations (multiple-satellite groups) that update data in nearly real time,” she says. “The idea that these small satellites—which fit into the palm of your hand—could deliver life-saving insights faster than traditional systems really inspired me to push the concept further.”

Last year Merchant and three of her classmates were accepted into MIT’s Beaver Works Build a CubeSat Challenge, where teams of up to five U.S. high school students were given eight months to develop a satellite capable of completing a space-based research mission.

Merchant’s team—the Satellite Sentinels—built a CubeSat powered by a convolutional neural network (CNN) that can identify heavily impacted flood zones and remotely collect data for disaster relief and environmental monitoring. CNNs analyze image data for pattern recognition.

Merchant was the group’s payload programmer and led the…