Welcome to the DirectionsMag podcast. We're honored to bring this valuable resource back to you as we celebrate our 20th year in the Geospatial community. Bill McNeil, our small UAV contributing editor is hosting a quarterly UAV podcast. In this edition, he interviews Jeffrey DeCoux and Adam Schlender from Hangar on scaling drone data and drone usage in public lands projects.
Bill McNeil: Can you give our readers a little background information on Hangar?
Jeffrey DeCoux: The history of Hangar kind of formed up, now almost 3 years ago. I came from an Enterprise Software background so was all about ways to automate things within business and how could technology improve business process and make business in general more effective. I learned about drones about 6 years ago, but it was only in 2014 that drones became something that can truly start impacting business because it transitioned really from the RRC and thumbs on sticks concept to now something that’s with the SDK that DJI released it can then now be completely automated with an autopilot and you can actually capture really valuable data on a very consistent and actionable basis. So, that was, there was a lot of history in Hangar gelling together, but the concept was around - you can leverage new robotics to capture valuable data and do it consistently.
The impact it has on many industries was going to be very large. And, what had to happen is that the drone itself had to disappear from being the key aspect of the conversation. It's not really about the drone, it's what the drone can do. And, for that to happen, you had to leverage autonomy, number one, for getting consistency, but then you had to have a complete data flow engine on the backend. Because the data being captured was not just valuable to creating maps and 3D point clouds, but there were so many other types of data assets that the drones could deliver upon and the amount of technology that could use that was large. So, Hangar did not see itself developing a data platform that housed all the interfaces for the client, we saw it as a delivery mechanism that allowed, what we call, 4D visual insights to be captured at scale and allow that data to be delivered into any number of systems on the backend in a very consistent, actionable way.
A lot of data that gets captured is valuable to Autodesk, Bentley, and the engineering and architectural apps. Other data that's captured is valuable to construction management software like Procore and Acrew, and other technologies. And then, we talk about the mapping. Just after disaster recovery, when you think about all the type of data that would be extremely valuable to emergency services and responding within cities, that type of data is valuable to systems like Esri, that cities use to monitor and manage recovery after big storms. So, Hangar started off realizing that there was a tremendous amount of engineering and development efforts that had to go into developing that platform out, and basically make it as simple as possible for businesses to leverage the incredible capabilities of drones, but not have to worry about incorporating in a new tool within the enterprise. An example would be after a storm. If we are tracking within the Hangar world, we have 55,000 cell towers from one vendor in the system. As we track with some partners storm events and what towers experienced certain characteristics of weather, whether it be winds over 80 mph or hard hail or turbulence like a tornado was in that vicinity. Those towers could all be automatically highlighted and from our standpoint we have missions that are automatically stamped down on assets and once those become highlighted as towers that are affected by a weather event, now all those become pins on a map for their service technicians or their operators as networks that they have authorized to be agents to go capture data for them out in the field. So, all the complexities of trying to dispatch people to go inspect towers in this case or the logistics of getting the right equipment or the right talent to right locations kind of just fades away and all they do is see is very consistent actionable data coming back to them through the Hangar platform.
Bill McNeil: Ok. All good stuff. What are the problems that users have with scaling drone data. How do you see these being resolved?
Jeffrey DeCoux: The scaling of drone data is because everybody tries to, I mean, as any new industry comes about, people are usually fascinated by the technology and not necessarily about the impact that the technology will have on the business. When you realize the impact of drones right now, it's not about the more complex things like delivery and other things that they will be doing in the future, it's about data collection. There was a time when planes captured a lot of data for people to find out what was happening at that macro level around the United States, and then satellites came and solved a grand scale. I think the same thing is kind of occurring with what drones can do. Each business acquiring and flying their own drones on a weekly or daily basis is a pretty cumbersome process, whereas if you have, like we do, a tremendous amount of partnering with large drone operating networks like Drone Base where they have operators all around the United States that go and capture many, many projects on a single day. And, when we look at our viewpoint, is on specific types on industries, they would like to have that data daily if the economics worked. And, we believe by solving the backend system of the data flow and always having an autonomous mission that’s being executed out of the location, then now networks like Drone Base can capture that data at scale and really start moving the dial on economies of scale of productivity and cost-savings inside of companies like construction and various infrastructure and even the telecommunications industry.
Bill McNeil: How is the Drone data changing public land and resource challenge?
Jeffrey DeCoux: I think Adam has done an exceptional job kind of managing that whole relationship. I think before Hangar, the likelihood they could get data at a scale that allowed them to apply just basic analytics to was virtually impossible. Adam, do you want to talk about TPL?
Adam Schlender: Yes, we have really great use cases that speak to that specifically, and it’s just a sort of add on what Jeff was saying, and kind of add a little resolution to what we mean by automating the process and enabling scale. When we refer to a mission, we're saying something that a drone is autonomously do within a span of a battery. Right, so there's this thing that's ultimately providing the value to the customer that the drone is doing completely everything autonomously over the span of 20-30 minutes. But, everything that must happen before that can happen, and before that imagery or that data product is delivered to a business, ends up being about 4-6 hours of human effort and labor. So, that is a lot of what our platform solves for and a lot of what enables that operator to fly 5-8 missions in a day as opposed to 2 or 3. That has 50,000 cell phone towers or is responsible for 30,000 bridges across the country or basically any type of large volume, large number of assets. The first sort of bottle neck in that process is you just need to be able to request, so here's the data that I need at these places on the earth. So, that's where working with Esri and sort of a GIS becomes the beginning of the powerful conversation. We're working with Trust for Public Land that has under their care and within their mission a little over 130,000 parks in the United States. Their mission is to ensure that every American is within 10 minutes of a high-quality park. And, to date, working with satellite imagery, in many cases, all they really know about a given park is sort of the boundary of the parcel. They may not know what sort of amenities if it has trails or basketball courts or what exists on that land, let alone, what condition are those amenities in today. So, this is a problem that imagery from drones can solve, but then they have the problem of capturing that data at scit. So, we've been able to programmatically be based on the features that they have in Esri, generate mission plans, and mission requests for basically all those parks. So, we've done an initial pilot in Dallas and basically, we're able to, in the span of a few hours, programmatically generate those mission plans and then engage volunteers to go out and collect that data which is then sort of just automatically published back with them.
Bill McNeil: So, when you are collecting features in these public lands and then it's basically temporal data, do you go back over and review those and fly those again?
Jeffrey DeCoux: Yes, that's the goal. The basic 4D temporal is to be able to track change over time and show improvements or degradation of parks or trails and stuff.
Adam Schlender: One of the features of our system, that Jeff mentioned, with construction for example, where they may want, a construction customer may want daily capture, you are able to request that data but then also over time. So, in the case of The Trust for Public Land, they may be requesting data quarterly, either a couple times a year they would want to make sure, kind of get a visual status update. In the case of construction, it may be daily for the 18-month life cycle of a project and is opposed to having to make that data request and every time you want new data, you must, you know, engage with a piece of software or try and find and source an operator. The Hangar platform allows you to basically schedule all that data collection in the span of a couple minutes and then the platform handles the rest. It removes that initial, that first barrier to scale which is just being able to say, here's the data out in the world that I need at the frequency that I need it and it releases that down to something that can happen in the span of a couple minutes and you can get data over time versus the alternative which is every piece of data needs to be requested individually.
Bill McNeil: Right. What about the different types? Are there different types of data if they are using your application for mission planning? Then, integrating the data is not a problem because it's all the same. Is that correct?
Jeffrey DeCoux: As far as what do you mean by data? Because there is a whole lot of different data asset types now within this. In construction, you can appreciate there's the standard things that a lot of people are familiar with like on a construction site. What surveys and kind of doing cut fill reports on the dirt movement? And then there are things like the ortho mosaics that are captured over time and kind of showing and tracking the logistics that are occurring. There are point clouds, but then there is all these other asset types which are included with working with the client where 360's happen to be a very valuable asset that all the people that are actually operating on the construction site. There is a handful of people that really appreciate point clouds and other types of data, but everybody likes 360's because it's basically from a visual acuity standpoint. It resonates exactly with the way they are thinking about the job site. The image series can also be used on parks where not only are you capturing 360's, but now you are capturing really high-resolution imagery that then can be, you can track it over time and easily detect things that change as things evolve. We do facade scans on buildings, which is another type of asset type where you're basically flying 10 feet away from the structure and capturing extremely high-resolution imagery that's used in safety risk mitigation. It's used in assessing quality of work on projects. There's thermal scans and we're now capturing intersections, when they are planning the logistics of how concrete on trucks and delivery trucks come in and out of a construction site. That data that we capture is then sent into algorithms that then actually show traffic patterns and how they are going to mitigate issues for the city to allow that project to progress forward. So, the more and more people learn about what Drones are doing and what they can do, the amount of data types starts to explode and the understanding of why a Drone should be planned on a construction site daily becomes obvious.
Bill McNeil: Right. Well, how does all this work? I know you are working with Esri and other GIS applications, so can you shed a little light on that and how your data works or will work with Esri applications?
Jeffrey DeCoux: I can share a couple ideas and I know Adam has, I mean we've now I think have 20 people involved with Esri because it's a deep partnership that I think is going to solve a lot of critical needs for the industries that we target. But, from construction standpoint, Esri is now leaning into the 3D kind of bridge between the BIM model that is being developed at that plat of land all the way to the completion where that now becomes a handoff to the owners and the city can then have that data at their fingertips to do planning within cities and just general management. So, I think from a standpoint of construction that's going to be a big push with AutoDesk that is being developed. So, this reality capture daily or weekly basis happens to play well into that whole plan. Our more exciting view on Esri is that pretty much every single federal state and local government uses some form of Esri to manage their cities and their states. And, if we can, like in Alabama Power have ever single of their transmission lines, distribution lines, substations, power plants, everything within the Esri system. We're now making calls, where we'll say that we need to make a call to find out, hey, what's all the power plants and we want to create a mission file that becomes a benchmark mission plan for that power plant to be inspected on a routine basis whether that be every 6 months or a year or 3 years. Esri kind of keeps that understanding of what assets they own, what assets need to be inspected, and allows us to attach our mission files to every single location.
And, public safety is another exciting thing as we've now demonstrated to several cities where they are getting excited because of the big push of disaster response last year. A lot of the public safety divisions inside cities are starting to get drones. Right now, they are doing a lot of manual flights and experimenting with how they can leverage for various situations. We're now demonstrating how let's say an accident occurs at an intersection, well we can show that all the officer must do is select that intersection, we can make a call to Esri, which then provides us all the details. We get back all the data about the buildings and structures and the angles of the roads and then we can automatically generate a mission plan that can now capture that whole accident scene in a robust way that's starting to show how Drones can be super actionable for public safety. It's all about how you can automate. How can you leverage the tool and not have to worry about what's being captured because you always capture the exact data that's necessary for any situation?
Adam Schlender: It just makes sense that the data request experience would begin inside of ArcGIS. That you already have all the information about your assets and where they are and the parcel boundary of the site and all that sort of information. The exact lat/long of where that cell phone tower is. So, it just seems very natural and makes a ton of sense that the experience of requesting data that the Hangar can provide would be in ArcGIS and as Jeff said, he features that of say a utility company, has about that power plant that already exists in ArcGIS, it gives us exactly what we would need to automatically generate the mission plan and dispatch that through the system. It also allows that data request to be made at scale. You know, I need inspection of these 10,000 towers and I need it once a month starting from geospatial data and then automate that process end.
Bill McNeil: We understand that you are working with The Trust for Public Land to solicit help from drone pilots all over the U.S. making 360-degree maps and thousands of public parks.
Adam Schlender: Yes, absolutely. It's been a great process. We began with 30 parks in Dallas that were immediately useful to them for their park serve initiative. We are with this joint R&B effort, kind of build out that capability to go from a GIS talking to our system to automatically generate those mission plans. So, we did a handful of tests and there are parks of all different sizes and shapes. Some of them are urban, some of them are more rural, we were kind of able to figure out a formula for a park of a given size and shape, you know, how many 360's do we need, how far do they need to be spaced out, what altitude is going to be the data set that provides them the insight that they need. So, after this sort of successful trial in 30 parks in Dallas, we're now scaling that to all of Austin and then we will scale it nationwide here in the next couple months.
Bill McNeil: Alright, so are soliciting work from the group of pilots that you have or that you know about? Is that correct?
Jeffrey DeCoux: Well, its family, its TPL leveraging their volunteers. The main thing is we are allowing them to leverage the technology to kind of prove out how the value of how this data being captured by the community and leveraging Hangar on demand makes that whole process as easy as possible.
Bill McNeil: It's very interesting and I wish you luck.
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