How Machine Learning Impacts Drone Technology

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Guest Post by Emily Folk

Without question, drones took the consumer market by storm. They’re every retired engineer and curious child’s favorite plaything. However, the impact of drones on the commercial landscape is only starting to unfold. From infrastructure inspection, site surveying and home delivery to agricultural and manufacturing implications, drones are a rich source of productivity and data.

Machine learning will take existing drones to even greater heights. They’re already extremely useful tools for observing our surroundings, but machine learning means that drones can, in addition to just looking at their environment, perceive and interpret their surroundings. Here are three major ways it’s already enabling change.

1. Improving Pattern Recognition for Automated Inspections

Physical structures like bridges and wind turbines are complex and imposing. Until now, it took large teams of highly trained engineers inspecting each part and installation to keep national infrastructure in good repair. But higher costs and improper political prioritization have delivered an increasingly poor return on our public investments.

Machine learning gives drones pattern recognition abilities. With programming and the right cameras and sensor equipment, they can safely, efficiently and even automatically provide ongoing and detailed inspections for large construction and infrastructure projects.

On a small scale, technology like this provides trained models that can be used to find product defects and appraise uniformity much more quickly and accurately while lifting existing QA workers into more cognitively demanding and higher-paying jobs. This development often comes without companies dropping any employees in the bargain. The same technological fundamentals easily scale to the size of tackling a nation’s infrastructure shortfall.

2. Optimizing and Planning Construction Site Activities

Construction site managers know only too well that they can’t have eyes everywhere at once. And yet, there are dozens of labor-intensive and often risky activities happening everywhere onsite, from pouring foundations to raising walls and re-contouring the land.

Automated or remote-piloted aerial drones are set to have a huge impact in environments like these. Using machine learning and trained models, construction drones could keep an eye on all of a construction site’s goings-on. They’ll be able to “understand” how the site changes daily and over the course of the project, deliver cost and timeline projections and help optimize the order and manner in which tasks are completed.

According to McKinsey’s “Imagining Construction’s Digital Future,” the nations of the world must collectively spend some $57 trillion on their infrastructure by the year 2030 to keep pace with worldwide GDP growth. The same report predicts that advanced surveying, geolocation and other technologies paired with drones will “dramatically improve [the] accuracy and speed” of these many necessary construction projects.

As drones take on more of the roles that construction site managers typically shoulder, it’s fair to expect that we’ll see a similar presence for drones in urban planning. Deploying drones to intelligently study vehicle and pedestrian patterns and make layout and traffic flow recommendations is almost certainly just around the corner.

3. Predicting and Interdicting Poaching and Other Crimes

Some cities in the U.S. have already banned, or are considering banning, facial recognition and other technologies from being used by the law enforcement community. According to activists, similar federal-level rules should be considered. However, advanced technologies have a lot to offer when it comes to predicting criminal behavior — at least in select circumstances.

As a prime example, the Lindbergh Foundation and a drone technology company called Neurala have an ongoing partnership to fight elephant poaching in Africa. Neurala claims that its software, which is powered by machine learning, can process in 20 minutes the same amount of surveillance footage that would take days or hours with previous-generation technology.

In the fight against poaching, the implication is that these “air shepherds” can automatically patrol vast amounts of natural landscape with very little guidance and recognize poaching activities before the trouble elements can reach wildlife refuges. And even when there aren’t any criminals in the picture, this level of intelligence is useful for finding and predicting the movements of protected animal populations or those with research potential.

Machine Learning and Drones: A Consequential Matchup

This is just a brief look at some of the ways machine learning is making commercial drones more useful, more observant, more productive and even more proactive. In time, we can expect that both drones and machine learning will have a presence in just about every corner of the public and private sphere, delivering even more impressive results as the technology advances.

Author Bio:

Emily is a green tech writer who covers topics in renewable energy and sustainable design. You can read more of her work on her blog, Conservation Folks.