How drones are changing data collection and analysis

The global drone analytics market is expected to grow by more than 17% annually over the next seven years, meaning it will triple in less than a decade. The growth of drones is largely due to the increasing sophistication and use of unmanned aircraft systems.

In fact, the use of unmanned aircraft systems has become a competitive advantage in many industries. Within a few years, drones had expanded into public safety and other markets as the need for efficient, low-cost data collection, processing and analysis expanded.

Over the past few years, however, the media has largely ignored the data side of drone technology, focusing instead on drone delivery development. This is actually an oversight. While delivery companies face considerable obstacles, drones are revolutionizing the collection and analysis of big data.

A Bird’s Eye View

Drone contractors are witnessing developments in how aerial robots collect, capture, organize, process and store large amounts of data. In essence, big data has moved out of the cloud and into the sky.

The cost of drone systems is an important factor driving their widespread adoption, and drone systems can be easily integrated with any use case you need. For example, a drone system can be sold to a police department for as little as $2,000 or more than $100,000. Either way, you're getting state-of-the-art technology that didn't exist 10 years ago.

Public safety has become one of the most productive use cases for drones in recent times. For example, law enforcement agencies across the country use drones to create 3D maps of accident and crime scenes, saving these agencies significant time, manpower, and money.

Previously, mapping a car accident or crime scene took three to four hours. Today, we have a platform that allows drones to autonomously perform mapping tasks with minimal input, and then downloads the data into a cloud-based modeling system. This can be done in minutes, not hours. Police departments can easily generate 3D images before their response to an incident is complete.

Contrary to public belief, these drones are not toys. In order to deploy drones in search and rescue, surveillance or even in pursuit of suspects, the entire system must be carefully constructed. Additionally, it must be set up to run in real time.

Typically, public safety clients start out doing things that are less time-sensitive, like mapping accident scenes. However, as they become more sophisticated, they may have dozens of drones deployed in the field at any one time.

In this way, once an alarm call is received, the drone will be automatically launched and sent to the scene, becoming the "aerial eyeliner" of law enforcement officials. Leadership will now have the ability to see the overall situation from a bird's eye view, whereas before, leaders could only be people on the ground.

Like self-driving cars and trains, the development of drones points to autonomous driving, but operating in the air brings special challenges and faces a unique regulatory environment. In the vast majority of cases, aviation authorities still require humans to take direct control of the drone and remain within the drone's line of sight at all times.

Theoretically, it's possible to get approval for drones, but so far it's been a labor-intensive exemption process.

Integrating AI and ML capabilities

It may be a slow process, but it is inevitable. Nearly all drone manufacturers are developing their own artificial intelligence and machine learning capabilities. Popular use cases include inspection and 3D mapping, not only of accident/crime scenes, but also development sites and existing structures. This is because GPS-enabled drones can be programmed within very specific contours, such as speed, altitude and physical boundaries.

Today we encounter an introductory version of ML, since it is usually based on just a few algorithms. The drone collects its own data as it flies and uses algorithms to adjust its programming.

For example, take a drone carrying a payload, which is a very stable platform when taking off without a payload. Adding a payload can be a little shaky at first. However, give ML a few minutes to figure it out and suddenly, it's back to a more stable state.

ML is critical to developing the modular capabilities of drones, where virtually any drone platform can be plugged into any type of functionality. The good news is that drones can now learn as they accumulate data on each flight, the bad news is that they need more learning to function around each other.

It is expected that in the future, drones will eventually become more like helicopters and airplanes, able to communicate with air traffic control and each other to avoid accidents, and even soon publish their own data.

While large-scale transportation via drones may still be a long way off, advances in unmanned aerial system technology will continue to largely remain under the public eye, which may Related to national labor shortages and new technological innovations.

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