What Every CIO Needs To Know About Commercial Drone Data

This post first appeared on Forbes.com as Drones Pose A Unique Big Data Challenge For Business Users

The public might consider them nuisances, but in the commercial market, drones are valuable data collection devices. Their primary task is to capture, store, and transmit data. So as IT departments consider integrating more drone data into existing enterprise business processes, they face new data governance requirements. As drone technology matures, it is important to know what it means for you as the steward of your firm’s information technology and software.

Drones present both a big data and an IoT challenge

Up to now, the focus of commercial drone use has been on accurate data collection and visualization—not IT process integration. To be fair, applications have been developed to support verticals like agriculture, construction, energy, mining, and telecom with cloud-based services, but these applications mostly produce and serve up maps, e.g., location maps for managing and servicing company infrastructure and other assets.

Just as with big data, the challenges of drone data include analysis, curation, search, sharing, storage, transfer, visualization, and information privacy. We are already beginning to see drones efficiently replace static IoT sensors with one device that is in motion and can capture multiple types of data (so not just pictures and video, but also emission gases, radio signals, geodetic data, etc.).

Is drone data that unique?

Like all IoT devices that are in motion, drones bring a lot of value and at the same time have a lot of challenges. For the most part, drone data is geospatial (or geographic data), imagery, videos, binaries, etc., so falls into the category of non-standard IoT data. However, if you work in IT, you’ll want to understand that this data has some unique requirements. F For example, it requires image recognition analysis and considerable transformation and data parsing to become useful.

A lot—if not most—of the data collected from drones can be used by geographic information systems (GIS).  GIS are mostly used for mapping and analyzing, and they integrate common database operations—such as query and statistical analysis—with visualization and geographic analysis. So, think mapping tools like esri’s ArcGIS.

Data governance implications

When dealing with drone data you may need to expand your current data governance policies because of new risks associated with aerial data itself (like privacy concerns) and the location and operations of the drone (because a drone is legally an aircraft and operates under certain regulations). For example, you may need to revisit policies regarding:

  • Source aviation system, its access, and APIs
  • Security and reliability along the “chain of custody” (drone service provider, to the cloud data service, to your front door)
  • Privacy and risk mitigation (legal issues)
  • Traditional Master Data Management (MDM) to straighten out the differences in reference data like location, asset type, customer name, etc.
  • Archive of source data for later re-processing (do you trust the custodian?)
  • Access control (who gets to see what and when?)

Learning a new lexicon

As you start integrating drone data, you should familiarize yourself with the most common types of processed “processed” data from drones—not the raw data, but the data produced by imaging software and the ones you’ll most likely come across if you’re in IT. Here are five examples:

An orthomosaic is an aerial photograph geometrically corrected (“orthorectified”) such that the scale is uniform: the photo has the same lack of distortion as a map. Typically, an orthomosaic is a composite of individual photos that have been stitched together to make a larger one. What you need to know is that the individual photos that make up orthomosaics each have their own georeference. The processed data (the composite) is what your end users want to use, but they may also want to know the location of the source data if it needs to be referenced later. Think about this in data governance terms. You may need to revisit your data retention rules if the source images are needed for evaluating changes over time.

Thermography (sometimes referred to as thermal imaging) uses thermal video cameras to detect radiation in the long-infrared range of the electromagnetic spectrum. Building construction and maintenance technicians can see thermal signatures that indicate heat leaks in faulty thermal insulation and can use the results to improve the effectiveness of their work. Thermal mapping is also “a thing” with vendors like DroneDeploy, which offers live streaming views, and can either be an image or a map.

Photogrammetry is a technique which uses photography to extract measurements of the environment. This is achieved through overlapping imagery, where the same feature can be seen from two perspectives. With photogrammetry, it is possible to calculate distance and volume measurements. Departments use these outputs to create “point clouds” or 3D images used to do things like render a building or measure the volume of a stockpile.

LiDAR stands for “Light Detection and Ranging.” It is a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth. These light pulses—combined with other data recorded by the airborne system—generate precise, three-dimensional information about the shape of objects and their surface characteristics. The accuracy of LiDAR images is stunning (we’re talking millimeters), which is why surveyors and construction engineers favor this technology. What you need to know is that LiDAR files are big. Datasets for a simple project area can be 1-2 TB.

Video is the most common and at the same time the most complex type of drone data. It’s complex because video is almost always stored in compressed form to reduce the file size for storage. A video file normally consists of a container format holding video data in a coding format alongside audio data in an audio coding format. Those are known as CODECs. The container format can also contain synchronization information and metadata such as GPS location and directional data, which can be encoded in each frame. 10 minutes of video at 30 frames per second = 18,000 frames. It’s complex because, when analyzing video data, you have to sort through all 18,000 pieces of frame data.

So here’s the big data problem—it’s the analytics. Most of what you want to know from images and video files (What can I see? What is happening? What is the value?) cannot be extrapolated by the traditional enterprise big data vendors. While automation can exploit this data and increase analysis efficiency, image and video analysis is more often done by teams of specialists. For this, you may want to outsource to an AI vendor that specializes in imaging or use an online drone data service.

 

Image credit: A SZ DJI Technology Co. drone is displayed during keynote presentations on artificial intelligence at the Microsoft Developers Build Conference in Seattle, Washington, U.S., on Monday, May 7, 2018. Photographer: Grant Hindsley/Bloomberg photocredit: © 2018 Bloomberg Finance LP

Why Drones Are the Future of the Internet of Things

What if you could talk to a drone?  No, seriously.  You can already talk to a locomotive, so why not talk to a drone?

For those of you following the technology, you already know that unmanned aircraft systems (a.k.a. drones) are finding their way into Internet of Things (IoT) implementations. IoT applications are typically composed of:

  • A sensor “at rest,” e.g., on a highway or a bridge or a thermostat that gathers input (like weather conditions or seismic activity)
  • A connection (via the Internet) between the sensor and a back-end data collection infrastructure
  • A back-end data collection infrastructure that’s commonly based in the cloud

So why do I claim that drones the future of IoT? For one, drone technology is evolving very rapidly. Drones are already beginning to efficiently replace the connected sensors at rest with one device that is:

  1. deployable to different locations
  2. capable of carrying flexible payloads
  3. re-programmable in mission
  4. able to measure just about anything, anywhere

To illustrate the trend and these capabilities, I’ll highlight the developments of several companies. But first – so that we are all on the same page – let’s look at what I mean when I talk about drones.

A New Kind of Drone

All drones are not equal. Some like the Global Hawk are very complex systems that are connected to satellites and are only the purview of the military. Others like the Parrot A.R. Drone are mass-produced hobby aircraft that you can control with your mobile device.  But a class of drones in the middle combines the capabilities of both complex and mass-produced systems and is specifically designed for commercial purposes. These drones weight less than 55 lbs. and are classified by regulatory entities as small unmanned aircraft systems or sUAS.  We don’t see their ubiquitous use in the U.S. quite yet, but in countries like England, Australia, and France, you will find them operating in energy, mining, mapping, and surveying companies – and quite a few government agencies like those responsible for transportation and infrastructure.

Commercial drones are truly ‘unmanned aircraft systems’.  They are not just remote controlled aircraft.  They require many things in order to run, like avionics, ground control stations, communication systems, data collection and processing software, and of course GPS for geo-referencing. There’s more, but you get the idea. These are multifaceted complex vehicles whose mission is to fly sensors and collect data.

Commercial drones are also connected devices. So they are ‘things in motion’. Most are accessible or controllable over the Internet, and the data they collect is pushed to various cloud services. Some drones are beginning to carry on-board processors as well and are now part of the growing trend of fog computing devices.

Deploy a Fleet

So, if a commercial drone is a connected device, then shouldn’t you be able to ‘talk to a drone’?  And shouldn’t you be able to – from your smartphone in California –control a drone in, say, France?

You can.  And it’s because companies like DroneDeploy and U|g|CS have figured out how to make addressable drone management platforms that control multiple drones from anywhere on any device.  DroneDeploy does it by marrying a simple 4G telemetry device to a drone’s avionics.  This enables real-time data transmission, processing, and sharing. With this kind of hardware and software combination, you can plan missions (launch, go to point A, then point B, then to point C, etc.) in a browser, upload them to a drone anywhere, press start, and away it goes.  You could do that with a fleet and monitor them all in flight.

Flexible payloads

So one of things commercial users want is the ability to mount different sensors such as thermal imaging, UV or multispectral cameras, sniffers, and microphones to sUAS. PrecisionHawk figured out early on how to offer an array of sensors that are hot swappable and just snap into place. The cool thing about their aircraft is that the body itself is made of circuit boards and processors.  They’re hardened of course on the outside, but it’s an example of the innovation happening in the commercial drone industry.

Reprogrammable in mission

So, not only can you deploy these anywhere, but they are reprogrammable while on a mission.  Let’s say you wanted to create a 3D map for a construction project and you programmed it to run its mission but in the middle you noticed something odd (because you are looking through the camera in real-time on your laptop or smart-phone). With SenseFly’s drone software, you simply point to that area on the map, and you can:

  • divert the drone
  • command it to perform another function in that area
  • then resume and complete its first mission
  • then come home and land

Measure just about anything

Every day, you can read about how measurement sensors are getting smaller and lighter. Such is the case with LiDAR, which allows you to capture minute details and measurements.  Because these units have been heavy up to now, there have been only three choices if you wanted these sensors to measure something:

  • They had to be stationary
  • They could be roving (stationary on a truck or SUV)
  • They could be carried on a manned aircraft

Stationary is the most accurate but lacks the significance of an aerial perspective.  You can get good results from aircraft, but not as good as from a drone.  With a drone can get close to the object – and as I mentioned they can be deployable on-demand. LiDAR manufacturers like Riegl and Velodyne get this, and we now see offered in the GIS market new high-performance, remotely piloted aircraft system for unmanned laser scanning, like those from Phoenix Aerial Systems and Sabre Systems. These airborne platforms provide full mechanical and electrical integration of sensor system components into aircraft fuselage.

LiDAR data models are huge, but as more low-cost in-memory computing becomes available, service providers are storing the models in the cloud and then updating them to reveal changes over time. Of course, it’s the analytics on top of that that provides the real insights – insights like structural integrity and predictive failures.  Soon, multiple infrastructure sensors – like those found on bridges and highways – will be obsolete.

What’s next?

We are only beginning to find out how drones can be used to replace multiple sensors, and hopefully I’ve successfully convinced you of how drones play into the future of the Internet of Things.  Surely this technology will push the bounds of how we can measure and analyze ‘things at rest’ and ‘things in motion’ and how they can interact with both of them.

You can find a companion SlideShare presentation to this post here.  I would love to hear your thoughts on this topic. Feel free to comment or write me at colin@droneanalyst.com.

Image credit: Shutterstock