This was originally published by Suzette Norris, May 2016 on RIT’s website.
The technology research firm Gartner says that, barring regulatory hurdles, the United States unmanned aerial vehicles (UAV) business could be worth $7 billion in a decade. What’s driving the growth? The Internet of Things—the idea of making physical things “smart enough” to provide intelligence reliably and cost-effectively.
Commercial industries around the world are starting to understand the power of this concept, and are looking to UAVs as one way of getting there. RIT, long known for its expertise in both aerial and satellite imaging, is also one of the world’s leading centers for research on UAVs.
As these two areas align, a new disruptive commercial model emerges. Low-cost UAVs collect information in new ways, and deliver it to software platforms capable of analyzing images and video reliably and in real time. The result produces insights that will dramatically change farming, insurance inspection, defense, law enforcement, disaster recovery, and many other industries.
But before that vision becomes reality, there are many technical and practical hurdles to overcome, including imaging science, engineering, and public policy, said David Messinger, director of RIT’s Chester F. Carlson Center for Imaging Science. That’s the reason RIT chose remote sensing with unmanned aerial vehicles as one of five strategic initiatives to receive $1 million for research. The interdisciplinary team, including imaging science, engineering, public policy, and computer engineering technology, will research ways to refine and integrate technology in ways that serve many different commercial and government needs.
When UAVs collect data, for example, there needs to be consistency in how the camera takes a picture, Messinger said. “Current instruments are not always stable, so we’re looking to build a system that uses inexpensive cameras to collect data in a precise way.”
Precision is critical, he said. Especially since many applications require “change detection.” Today, humans are paid to watch hours of surveillance video looking for meaningful change between one image and another. Programming a computer to do this is a difficult problem to solve. Farmers practicing precision agriculture, for example, want to collect real-time data and use predictive analytics to make smarter decisions.
“But they don’t want to stare at video to determine where a leak in their irrigation system is,” Messinger said. “They want reliable usable information every day.”