Commercial drone use has emerged as an important tool for asset inspection across multiple large industries. From the very early days of simply providing imagery from unique perspectives to where we are today with drone data analysis, commercial drone use is now mainstream. But that is nothing compared to where we are going. The future of drone inspection services will revolutionize whole industries and entire workflows. This future is within sight and it’s exciting to explore.
In this blog, I review where commercial drone use started, how drone data analytics became critical to drive adoption, and paint the vision for the future of drones for automated asset inspection.
The Past - Early Days of Commercial Drone Use
Commercial drone use has grown significantly since its inception less than a decade ago. In the Fall of 2016, the FAA issued the Part 107 rule, which clearly defined requirements to allow drones to be used for commercial purposes in the US. We went from a handful of drones registered for commercial use in 2016 to more than half a million by 2022. The initial commercial use of drones focused on capturing imagery from unique perspectives – a natural extension of recreational drone use. This included creative marketing photos for real estate, agriculture, and simple surveying.
In those early days of commercial drone use, a wave of drone hardware and drone management startups emerged to drive the industry forward. Pioneering startups included Kespry, with hardware and flight control solutions, DroneDeploy, with drone enablement, Dronebase, with drone flight services, and Pointivo, offering advanced analytics behind many of these companies. These companies were conducting Proof of Concepts (POCs) in industries like insurance, construction, residential and commercial roofing, utilities, among others. Over time we saw the demise of some of the early startups who could not find sustainable business models.
Those early years were focused on enabling the commercial drone ecosystem to work, which included testing different drone platforms, learning how to fly proper flight patterns, figuring out who would fly, managing data uploads, and determining what could be done with the data. The primary offering was images with new views that were previously difficult or expensive to obtain, along with a 3D model and some measurement tools for the end users. For example, the mining industry found great benefit in stockpile volume calculation using drones and the early volumetric measurement tools offered with 3D models.
As innovative enterprises completed POCs, the serious ones raised the crucial question: “what can we do with that data; can it help us deliver better, faster, cheaper?” Data alone was not enough; analytics on the data to streamline business operations became critical to driving drone adoption. The drone analytics industry quickly became a critical part of the commercial drone ecosystem.
Pointivo, as the early leader in drone analytics, combined machine learning and computer vision to automatically generate measurements of residential roof systems. The major drone platforms gained momentum in roofing and insurance by utilizing Pointivo’s drone data analytics platform. Major insurance companies ran programs to explore drone use to improve claims processing. Some utilities funded massive POCs in distribution line asset inventory and inspection and the construction industry started exploring site progress reporting.
Some key advances in the commercial drone ecosystem emerged from this phase:
- A single dominant drone hardware platform (DJI)
- Autonomous flight became commonplace
- Drone pilot networks, though still early and scattered, were formed
The Present - Commercial Drones and Analytics
Today, we are in the second phase of commercial drone adoption. Commercial drone usage has grown from POCs to production at scale, especially in roofing, construction, renewables, energy, and telecom industries. The growth has been driven by the early adopters finding applications where analytics provide a real ROI for their businesses.
In the roofing industry, residential solar installation leaders realized they could scale faster, better, and cheaper by incorporating drones into their process. Enterprise solar companies like Sunrun are using drones to improve worker safety, speed up the quoting process, and greatly reduce errors/mistakes. These benefits are realized partly because they have a complete solution delivering pilot training, automated flight and data upload, and advanced analytics to automatically generate engineering drawings of a roof to feed their proprietary solar design software. The solution requires cooperation among partners who are each experts at their different segments of the commercial drone ecosystem. Pointivo, as the key analytics component of the solution, is processing over 10,000 roofs per month through its patented automated roofing/solar analytics and measurement platform.
In the telecom industry, some of the largest companies, including American Tower and DISH, have incorporated drones into some key workflows. Production applications include tower asset inspection and pre- and post-construction inspection. Industry leaders have taken different approaches to the flight portion of the solution, from building their own in-house flight services team to outsourcing all flight operations.
Increased safety alone is a huge benefit in the telecom market where significant workforce shortages and the demand for accelerated wireless technology roll-out like 5G is commonplace. Some telecom industry leaders are benefiting from advanced AI-driven tower analytics with automated equipment detection, automated contract auditing, deep enterprise system integration, and other features to streamline entire business processes. Pointivo is processing over 3,000 towers per month through its automated tower analytics platform. This is just the beginning in the telecom infrastructure space, as we are seeing growth with nearly every major participant across all market segments.
In the infrastructure industry, we are seeing growth, but not yet at scale, partly due to the challenge and expense of flying in these environments. Drone hardware platform companies, like Skydio, are moving into the commercial drone arena to address the challenge of flying these complex structures. However, we are still early in the adoption lifecycle and the analytics are still emerging.
Although commercial drone adoption has increased and is running at a massive scale in some industries, it is not yet at the scale many expected. But we are seeing a very clear trend: advanced analytics are critical to drive business value and expectations for analytics are rising in every market.
Enterprise-level customers are getting smarter and asking hard questions, such as “how does this make me faster, cheaper, and better while requiring minimal change to my current processes?” It is increasingly difficult for vendors who are simply providing “pretty 3D models” and manual tools to get meaningful traction. They are struggling to get out of the cycle of endless POCs. Many of those vendors claim to use AI, but few have delivered sufficient real business value to drive enterprise adoption. In order to reach the potential of commercial drone use, analytics must improve.
In the telecom space, for example, engineering firms don’t want to learn new tools and don’t have time to change their processes. They are asking for automatic generation of their current reports, not new tools they could use to generate reports. Pointivo recently released its five day mount mapping product in response to this demand. By generating complete and accurate mount mapping reports from drone captured data using AI, and providing importable data files to AutoCad and RISA-3D, we are gaining traction with every leading telecom engineering firm.
Commercial drone use has become mainstream over the last few years, and some industry leaders are delivering at scale. In order to continue this trend across different industries, the demand for improved analytics and automation must be met. Only by delivering solutions with powerful drone analytics that reduce the customer’s workload and make them better, faster, and cheaper, can we expect drones to become a critical component of enterprise workflows.
The Future - Fully Automated Inspection Workflows
Over the next decade, we will see a real transformation in how drones are used across many industries. The future of commercial drone use will be driven by automation. This automation will come from continued improvement in automated flight, data management, and especially in drone analytics.
Today’s commercial drone solutions still involve someone requesting the service, someone traveling to a location to capture data and sending data to an analytics platform, followed by someone reviewing the results.
In the near future, people will be relieved from performing repetitive inspection-related tasks through advances in AI-driven analytics. Instead of someone initiating and controlling data capture, the system of the future will be fully automatic with minimal oversight. People will get involved only when there is an exception that cannot be resolved by the AI engine. Drones and related analytics will become fully integrated into standard automated workflows that no longer require human interaction. The solutions will include not just drone-captured data but also integrated mobile, thermal, environmental, and other sensor data.
Drones will fly automatically on a schedule or drone flights will be initiated by an event. Events could include a storm of a certain severity passing through an area, modifications to a site, system health sensor alerts, and more. A drone will automatically launch from the closest permanent location or from Uber-style on-demand launch pads.
The drone will fly, capture, and upload data with zero human involvement. The permanent locations, or drone-in-a-box deployments, initially will be strategically placed to provide full geographic coverage, such as at all cell tower sites. Eventually, they will expand to post offices, major retailers, and other locations. Specialized analytics engines will automatically identify any change in the structure or condition, combine data from other sensors or video feeds, and choose any next steps in the process.
The analytics engine will decide whether to 1) simply log the data and analysis and store it, 2) initiate a service call or follow-up event to address identified issues, or 3) send an alert for human review of results in real-time or asynchronously. And if human review is needed, the system will prepare all data in an optimized decision-making format such that the review time will be minimal, and the review completion automatically initiates the next steps in a process.
Let’s consider some real world scenarios in the telecom and construction industries.
In the construction space, at a minimum on a daily basis, drones will fly to cover the exterior and some large interior spaces, robots will traverse interior spaces, and contractors will capture any intricate areas via mobile devices. The analytics engine will update the as-built Building Information Model (BIM) and compare it against the as-planned BIM model and the planned construction schedule. The system will update all scheduling and progress reporting systems as well as raise alerts when any issues are found, schedule review of results, and optionally schedule follow-up steps.
In the telecom industry, any time a macro tower site is accessed, a drone will deploy from the box at the base of the tower and do a full tower capture, confirming with backend systems the purpose of the site access (e.g., carrier equipment upgrade). The system will capture and confirm that the tower is safe to climb and free of wildlife.
After the modification, the drone will confirm that all changes were made as planned according to construction documents, log results to appropriate downstream systems, produce required reports for stakeholders, and confirm that all safety systems remain in place and the site was left as expected. Everything is automated and no human interaction will be required for these inspection style processes.
This future world of analytics will require significantly advanced AI capabilities combined with a full network of deployed drones, ubiquitous broadband or high speed wireless, truly autonomous flight, and smooth data flows across all ecosystem components. And it will require strong partnerships between providers as no individual vendor will be able to offer the complete solution.
Pointivo was founded to fulfill this vision and has the advanced drone analytics platform to enable this future. We will continue to work to work with the best partners and industry leading companies to reach the ultimate potential of commercial drone use.
The Digitization of the Tower & Roofing Industry
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About the Author
Dan is the CEO and cofounder of Pointivo, the leading AI analytics platform for physical asset inspection. Pointivo was founded eight years ago with the vision to automate analytics using its patented AI and computer vision platform. As Pointivo has been at the forefront of the commercial drone industry, Pointivo has focused solely on providing advanced analytics in support of customers and partners transforming their industries. Pointivo has grown to be #1 in telecom, roofing, and facilities analytics processing over 120,000 assets annually, and is expanding to energy, utility, and infrastructure markets. Given its early entry in the space, Pointivo’s extensive patent portfolio gives it broad ownership of many of the enabling technologies required to drive the growth and future of automated asset inspection.
