Drone Mapping – Business Models Revisited

I am currently attending the 2017 NSSGA/CONEXPO exposition.  One of the keynotes from the National Stone, Sand and Gravel Association (NSSGA) conference focused on the rate of change of technology in the mining industry and the scope of operations that are covered by these technologies.  Of course, one of the examples was the use of drones.  The gist of the discussion was that some of these technologies are in their formative stages; we do not yet fully appreciate the scope of operational affect they will have but to prosper, knowledge of these systems must be internalized.

One thing is very clear – frequent and repetitive mapping will be required to support the automated machinery that is now appearing on advanced sites.  You cannot program a haul truck for autonomous operations if you do not know the location of the road!  Complicating this issue is the fact that the road location changes nearly daily due to the operation itself.

This future trajectory says that mine site mapping will need to become an internal operation.  It will be impractical from both a logistics and cost perspective to outsource drone mapping services.  A second strong consideration is the rapidity with which drone technology is changing.  I think amortizing the cost of a drone over more than 12 months is just not realistic.

Drones are simply platforms for cameras and other sensors (for example, profilers, laser scanners and so forth).  A drone without a sensor is a fun toy to fly but it is not going to have much use in operations!  I am very excited about new platforms from commercial drone companies (mostly DJI).  These new drones include decent cameras in that they now incorporate larger sensors and hybrid shutters.  You can do a reasonable job of mapping with these yet still use them for inspection videos.

DJI Inspire

So I think what we are seeing is the beginning of the end of the purpose-built drone.  You will be able to purchase drones from DJI (and perhaps others) that are nearly a consumable.  You can use the same drone for inspections as you use for mapping.  This is a very important consideration since this greatly simplifies the training of users.

The bottom line here is this – we are seeing the beginning of drones as an everyday tool for mining, industry and construction.  The proper model is going to be internal control of not only flying the systems but also processing the data.  When you need a quick check of a pulley on a conveyor, you will want an internal staff member to quickly fly the inspection job and post the resultant video.  No need to have a third-party system or contractor involved.  It just complicates the flow and adds expense.  This is really the motivation behind our Bring Your Own Drone (BYOD) Mapping Kit.  It lets you use a low-cost drone such as the DJI Inspire to do serious mapping without a lot of complicated leasing or outsourced data processing arrangements.  It also allows you to use the same platform for inspection that you use for mapping.  Give us a call to see how well this solution will meet your specific needs.


AirGon Happenings

I am pleased to announce that AirGon’s request for amendment to its Section 333 waiver for flying commercial small Unmanned Aerial Systems (sUAS) was approved in April.  Our amendment adds all current and future 333 approved aircraft to our 333.  AirGon can now fly any sUAS that has ever been approved by the FAA as well as all future approved systems.  This list currently contains 1,150 different sUAS (AirGon’s own AV-900 is number 207 on the list).  This provides us a lot of flexibility in working with clients; for example, in situations where a glider sUAS is more efficient than a rotor craft.

The FAA has also recently streamlined the process of obtaining an N number for a sUAS.  Prior to the change, a paper process that required several months was the only option.  Now an online system is available, greatly simplifying this procedure.  Note that this is not the new online registration system for hobby drones but rather the system used for obtaining an N number for a manned aircraft (if you are confused, join the club!).  Combined with our new 333 amendment, we can now get a new aircraft legally operating within days.

We continue to do a lot of work to optimize the accuracy of point clouds derived from dense image matching (DIM).  DIM are the data of choice for sUAS mapping since they can be generated from low cost prosumer cameras using standard application software such as Pix4D Mapper or PhotoScan.  The question always remains as to how good these data really are.

It has taken us a lot of experimentation and analysis but we think we have fleshed out a procedure for assuring good absolute vertical accuracy.  It involves the use of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) positioning on the sUAS, a local base station that we tie into the national Continuously Operating Reference Station (CORS) network and the National Geodetic Survey’s Online Positioning User Service (OPUS) to “anchor” the project to the network.  We have also discovered that high vertical accuracy cannot be obtained without camera calibration.  We typically use an in situ process for calibration.  We have flown many dozens of sites (primarily mining), giving us a rich set of test data.

I cannot over emphasize how critical network vertical accuracy is.  Most customers want elevation maps of their sites.  These are usually delivered as contour vector files.  As we all know, a 1 foot contour requires vertical accuracy of 1/3 of a foot.  This is a very tight requirement!  A three inch vertical bias error over an acre is an error of about 400 cubic yards – this is significant.

We see a lot of drone companies processing site data with no control and no RTK/PPK.  While, with the introduction of scale into the model (many companies do not even do this), one might obtain reasonable difference computations (such as volumes), the network accuracy is very poor (obtained from the airborne navigation grade GNSS only) and hence the data are of limited use.  We have discovered that these techniques (where no control and/or RTK/PPK is used) can result in the vertical scale being incorrectly computed.  This means that even differential measurements are not accurate.  Why spend all of the money to collect these data if they are of unknown accuracy?

A more difficult area that we have studied over the past several years is what I refer to as “conformance.”  That is, how well does the DIM actually fit the object being imaged?  DIM processing software (again, such as Pix4D and PhotoScan) do a miraculous job correlating a 3D surface model from highly redundant imagery using the general class of algorithm called Structure from Motion (SfM).  In addition to the obvious areas where SfM fails (deep shadow, thin linear objects such as poles and wires), a lot of subtle errors occur due to the filtering that is performed by the SfM post-extraction algorithms.  These filtering algorithms are designed to remove noise from the surface model.  Unfortunately, any filtering will also remove true signal, distorting the surface model.

We are working with several of our mining customers to quantify these errors and, once these errors are characterized, to develop best practices to minimize or at least recognize when and where they occur.  An example of an analysis is shown in Figure 1.  Here we are analyzing a small pile (roughly outlined in orange) of very coarse aggregates with a volume of about 340 cubic yards.  This site was flown with a very high end manned aircraft LIDAR system and with AirGon’s AV-900 equipped with our RTK system.  The DIM was created using Agisoft PhotoScan.  We obtained excellent accuracy as determined by a number of signalized (meaning ground targets visible in the imagery) control and supplemental topo only shots.  We used in situ calibration to calibrate the camera (a Sony NEX-5 with a 16 mm pancake lens).

As can be seen in Figure 1, we created a series of cross sections over the test pile.  These cross sections were generated using the Cross Section Point Cloud Task (PCT) in LP360/Topolyst.  This tool drapes cross sections at a user specified interval, conflating the elevation value from the user specified point cloud.  We ran the task twice, conflating Z first from the LIDAR point cloud and then from the DIM.   In Figure 1 we have drawn a profile over one of the cross sections with the result visible in the profile view.  The red cross section is derived from the LIDAR and the green from the DIM.

Comparing LIDAR (red) to DIM (green)

Comparing LIDAR (red) to DIM (green)

Note that the DIM cross section (green) is considerably smoother than the LIDAR cross section (red).  This is caused by several factors:

  • The aggregate of this particular pile is very coarse with some rocks over 2 feet in diameter. This leaves a very undulating surface.  The LIDAR is fairly faithfully following this surface whereas the DIM is averaging over the surface.
  • The AV-900 flight was rather high and the data was collected with a 16 mm lens. This gave a ground sample distance (GSD) a little higher than is typical for this type project.
  • Due to the coarseness of the aggregate, significant pits appear between the rocks, creating deep shadows. SfM algorithms tend to blur in these regions, rendering the elevation less accurate than in areas of low shadow and good texture.

The impact of lower conformance is a function of both the material and the size of the stockpile (if stockpiles are what you are measuring).  For small piles with very coarse material (as is the case in this example) a volumetric difference between LIDAR and SfM can be as great as 20%.  On larger piles with finer aggregates, the conformance is significantly better.   For example, in this same test project, we observed less than 0.25% difference between LIDAR and the DIM on a pile of #5 gravel containing about 30,000 cubic yards.

There still remains the question of which is more accurate – the volume as computed from the LIDAR or the volume as computed from the DIM?  I think that if the LIDAR are collected with a post spacing ½ the diameter of the average rock, the LIDAR will be the most accurate (assuming that it is well calibrated and flown at very low altitude).   However, the DIM is certainly sufficiently accurate for the vast majority of aggregate volumetric work, so long as a very strict adherence to collection and processing best practices is followed.  For most high accuracy volumetric projects, manned LIDAR flights are prohibitively expensive.

We continue to do many experiments with local and network accuracy as well as methods to improve and quantify conformance.  I’ll report our results here and in other articles as we continue to build our knowledge base.

Your Business Model, not Ours!!

We have invested a tremendous amount of resources (monetary, development, knowledge) into developing technology and services for mapping sites using dense image matching collected with small Unnamed Aerial Systems (sUAS). Our focus is applications suitable for an sUAS (non-populated areas, smaller sites) that require near survey grade accuracy. The most common example is small open pit mine sites such as quarries. We have not considered agricultural applications since these tend to be very large areas where radiometric analysis is the focus rather than geometric correctness.

Like most other companies involved in this emerging market, we are trying to predict the most palatable business model. However, I would say that unlike many other technology providers, we are seeking the business model that makes the most sense for the customers, not for us.

AirGon LLC has a very big advantage over companies funded by venture capitalists. We are funded both by GeoCue and by investments from our small group of inside shareholders. This allows us to focus on a long-term vision of the market. We plan to become the “go to” company for sUAS mapping, much as GeoCue has become the “go to” company for airborne and mobile laser scanning.

One of the big questions that Venture Capitalists have in funding a startup is that of scale. If the venture will not scale up to a sufficient size to provide a comfortable multiple on the initial investment, the venture is not considered financially viable. In the sUAS business, it is hard to devise a model that will scale that does not require significant involvement on the part of the customer. The most popular model is a leased plan where the customer flies the drone and uploads the image data to a cloud-hosted system provided by the vendor. In some of these models, the customer may even do the data extraction such as defining the base of a volumetric stockpile.

These “self-service” business models proliferate in the rollout of new technologies that are generally called “Web 2.0” (or are we Web 3.0?). You now see it with everything from reservation booking systems to the Uber taxi concept (in the Uber case, the job of “dispatch” has been handed over to the customer). Even grocery stores have gotten on this bandwagon with self-service checkout kiosks.

We certainly believe that self-service will play a major role in the emerging sUAS mapping business. However, at the current time one size does not fit all. This is particularly true in light of the draconian FAA regulations that currently exist for commercial sUAS operations. A mine site leasing a “fly-it-yourself” drone would require an FAA 333 exemption as well as an FAA licensed pilot. This is a fairly significant barrier to adoption of the technology. In addition to the legal hurdles, many customers want to nibble into this new approach to mapping rather than wolf it down in one gulp.

We launched our CONTINUUM program as a way to address these customer needs. CONTINUUM allows a customer to pick from a menu of hardware, software and services that best suit her needs. A few customers want to buy a mapping kit and do it all themselves. For this customer we offer the AV-900 Metric Mapping Kit (in both base and RTK versions). Other customers want to fly their own equipment but have the data processed as a service. Still others want to have us provide full services where our Field Service Analyst shows up at their site and performs the complete job. Under CONTINUUM, we can provide what the customer wants, not what we think might be the best business model for us.

One of the real values behind CONTINUUM (and the reason for the name) is that most customers do not know what they will want to do as a final business model. They would like to be in an environment where they can experiment a bit. This is exactly what we provide through the CONTINUUM program. A customer can modify the business model from one of AirGon doing everything to they, themselves,  internalizing the entire process or any mix in-between, all without the need to change vendors.

I am not sure what will be a profitable business model for AirGon. We are still very heavily in the Research and Development mode. However, one thing I do know for sure – the successful business model will be the one that is deemed successful by the end-use customer. We intend to be the provider of that ultimate solution!