An option is something you can do but donāt have to do. All our product ideas are exactly that: options we may exercise in some future cycleāor never.
Without a roadmap, without a stated plan, we can completely change course without paying a penalty. We donāt set any expectations internally or externally that these things are actually going to happen.
I know Basecamp is always the industry outlier with these things, but the thoughts on roadmaps are probably more true for many companies in reality than weād all like to admit. We tend to look at things in a sort of hybrid way āĀ not a fully baked roadmap with timelines, but a general list of roughly-sorted candidates that gain more and more momentum as we shape them out and prioritize. Every product team has a list of ideas 10x+ longer than anything they can build, so optionality is required to make the right decisions.
One of my favorite open source projects ongoing right now is OpenDroneMap. I havenāt gotten to use it much recently, but followed development over the last couple years. Outside of some loose testing a while ago, I havenāt flown my Mavic for any imagery collection since. I need to go out to the waterfront nearby and fly some new data so I can kick the tires some more on ODMās latest stuff.
Piero just announced completion of contour support in WebODM, which is the web front-end to the ODM processing suite. This is some powerful geospatial software, and a great example of what a committed team can do with open source. The new contour capability leverages the GRASS engine as well as GDAL to process, then allows you to export to GIS. I canāt wait to try it out.
My colleagues Bill Dollins and Todd Pollard (the core of our data team), wrote this post detailing how we go from original ground-based data collection in Fulcrum through a data processing pipeline to deliver product to customers. A combination of PostGIS, Python tools, FME, Amazon RDS, and other custom QA tools get us from raw content to finished, analyst-ready GEOINT products.
The 518 coordinated flights operation, by 16 Northern California emergency responder agencies, is one of the biggest drone response to a disaster scene in the nationās history. The 16 UAV teams were led by Alameda County Sheriffās Office. Stockton Police, Contra Cost County Sheriffās Office & Menlo Park Fire Protection District had the most team members present, with Union City Police, Hayward Police and Stanislaus County Sheriffās Office providing units as well. San Francisco Police oversaw airspace mitigation. In addition to the mapping flights, over 160 full 360-degrees and interactive panoramas were created with the help of Hangar, as well as geo-referenced video was shot along major roads in Paradise through Survae.
An impressive effort by response agencies in California to respond to this tragic disaster and assess the damage.
An article on the Great Unconformity in the geologic record and its potential cause:
The Grand Canyon is a gigantic geological library, with rocky layers that tell much of the story of Earthās history. Curiously though, a sizeable layer representing anywhere from 250 million years to 1.2 billion years is missing.
The likely culprit was a theoretical planetwide glaciated period known as the āSnowball Earthā.
Since I got the Mavic last year, I havenāt had many opportunities to do mapping with it. Iāve put together a few experimental flights to play with DroneDeploy and our Fulcrum extension, but outside of that Iāve mostly done photography and video stuff.
OpenDroneMap came on a scene a couple years ago as a toolkit for processing drone imagery. Iāve been following it loosely through the Twittersphere since. Most of my image processing has been done with DroneDeploy, since weād been working with them on some integration between our platforms, but I was curious to take a look once I saw the progress on ODM. Specifically what caught my attention was WebODM, a web-based interface to the ODM processing backend ā intriguing because itād reduce friction in generating mosaics and point clouds with sensible defaults and a clean, simple map interface to browse resulting datasets.
The WebODM setup process was remarkably smooth, using Docker to stand-up the stack automatically. All the prerequisites you need are git, Python, and pip running to get started, which I already had. With only these three commands, I had the whole stack set up and ready to process:
Pretty slick for such a complex web of dependencies under the hood, and a great web interface in front of it all.
Using a set of 94 images from a test flight over a job site in Manatee county, I experimented first with the defaults to see what itād output on its own. I did have a bit of overlap on the images, maybe 40% or so (which you need to generate quality 3D). I had to up the RAM available to Docker and reboot everything to get it to process properly, I think because my image set is pushing 100 files.
That project with the default settings took about 30 minutes. It generates the mosaicked orthophoto (TIF, PNG, and even MBTiles), surface model, and point cloud. Hereās a short clip of what the results look like:
This is why open source is so interesting. The team behind the project has put together great documentation and resources to help users get it running on all platforms, including running everything on your own cloud server infrastructure with extended processing resources. I see OpenAerialMap integration was just added, so Iāll have to check that out next.
For his final weekly column of his long career, Walt Mossberg talks about what he calls āambient computingā, the penetration of IoT, AR, VR, and computers throughout our lives:
I expect that one end result of all this work will be that the technology, the computer inside all these things, will fade into the background. In some cases, it may entirely disappear, waiting to be activated by a voice command, a person entering the room, a change in blood chemistry, a shift in temperature, a motion. Maybe even just a thought. Your whole home, office and car will be packed with these waiting computers and sensors. But they wonāt be in your way, or perhaps even distinguishable as tech devices. This is ambient computing, the transformation of the environment all around us with intelligence and capabilities that donāt seem to be there at all.
Great piece from Chris Anderson on the prospects of the commercial drone space. He makes great points about the true success of the technology being its penetration into business applications:
Although it might surprise you, I hope the future of drones is boring. As the CEO of a drone company, I obviously stand to gain from the rise of drones, but I donāt see that happening if we are focused on the excitement of drones. The sign of a successful technology is not that it thrills but that it becomes essential and accepted, fading into the wallpaper of modernity. Electricity was once a magic trick, but now it is assumed. The internet is going the same way. My end goal is for drones to be thought of as just another unsexy industrial tool, like agricultural machinery or generators on construction sites ā as obviously useful as they are unremarkable.
Another good reminder from Fred Wilson on the importance of focus. He suggests setting no more than 3 ābig effortsā in a year, the āmust dosā. More than that is lying to yourself and losing steam on the ones you really care about:
But regardless of whether you have two, three, or four big efforts this year, you should test all of your initiatives agains the āmust doā vs ācan doā test. Just because you can do something doesnāt mean you should. Iāve written about the importance of strategy and saying no. Strategy isnāt saying no. It is figuring out what is the most important thing for your company and deciding to focus on it and say no to everything else.
At work weāve been building an integration between Fulcrum and DroneDeploy, a service for automating drone flight and data capture for aerial imagery. Itās compatible with the Mavic, so I gave it a shot with some test flights over my house.
The idea is simple: use DroneDeploy to draw on a map the area you want to survey from above, and their app handles building the flight plan, sending it to the drone, and flying the waypoints to take all the photos. You then take the pictures from the droneās storage and upload to your DroneDeploy project for processing. It stitches them into a single mosaic and does a few other data processing functions to give you maps of NDVI plant health, elevation, and even a 3D model of the scene.
This data is from a 3 minute flight over my house at about 150 feet. The post-processed scene reports 0.75 acres at 0.6 in/pixel resolution. Only 13 stills required to create this image. Itās pretty impressive for a few minutes of setup and a few minutes of flying. In the full-res images you can actually see Elyse and I clearly standing in the backyard. She was a little spooked as it took off, but loved the landing!
Using Amazonās Athena service, you can now interactively query OpenStreetMap data right from an interactive console. No need to use the complicated OSM API, this is pure SQL. Iāve taken a stab at building out a replica OSM database before and itās a beast. The dataset now clocks in at 56 GBzipped. This post from Seth Fitzsimmons gives a great overview of what you can do with it:
Working with āthe planetā (as the data archives are referred to) can be unwieldy. Because it contains data spanning
the entire world, the size of a single archive is on the order of 50 GB. The format is bespoke and extremely specific
to OSM. The data is incredibly rich, interesting, and useful, but the size, format, and tooling can often make it
very difficult to even start the process of asking complex questions.
Heavy users of OSM data typically download the raw data and import it into their own systems, tailored for their
individual use cases, such as map rendering, driving directions, or general analysis. Now that OSM data is available
in the Apache ORC format on Amazon S3, itās possible to query the data using Athena without even downloading it.
Personal plug here, this is something thatās been in the works for months. We just launched Editor, the completely overhauled data editing toolset in Fulcrum. I canāt wait for the follow up post to explain the nuts and bolts of how this is put together. The power and flexibility is truly amazing.
The team at DroneDeploy just launched the first live aerial imagery product for drones. Pilots can now fly imagery and get a live, processed, mosaicked result right on a tablet immediately when their mission is completed. This is truly next level stuff for the burgeoning drone market:
The poor connectivity and slow internet speeds that have long posed a challenge for mapping in remote areas donāt hamper Fieldscanner. Designed for use the fields, Fieldscanner can operate entirely offline, with no need for cellular or data coverage. Fieldscanner uses DroneDeployās existing automatic flight planning for DJI drones and adds local processing on the drone and mobile device to create a low-resolution Fieldscan as the drone is flying, instead of requiring you to process imagery into a map at a computer after the flight.
I bought a Mavic Pro a couple weeks ago and just got a chance to take my first flights this past weekend. In short, itās the most impressive technology product Iāve used in years. Iāve never owned any drone, so this is pretty cool for someone in the mapping industry. Letās dive in.
Since going out to fly aerial mapping missions with some partners of ours a couple months back, I wanted to buy one of DJIās drones ā either the larger Phantom 4 Pro, or the smaller Mavic. Extensive research led me to the portability and almost-equivalent technical specs of the Mavic over the P4. Itās so close in most of its capabilities, but the compactness of it is remarkable. I got the kit with the carrying bag, and itās so small you could literally take it anywhere. I love the prospect of having this as a photography platform while traveling.
I did my first test flight in the backyard, plopped it down on the patio and kicked on the drone and remote control. Everything linked up right away and the DJI Go app was āReady to Flyā. Itās so simple it seems like youāre doing something wrong. It feels like there should be more configuration. As long as youāve got a clear GPS signal and youāre in ābeginnerā mode, you can just take off.
My first reaction was how easy it is to fly. You donāt have to do anything and the drone just hovers. Let go of the controls at any time and it stays put. The controller sensitivity feels smooth and intuitive; I was strafing sideways, rotating, and descending to create cool sweeping shots within 2 minutes. With a little practice you could do pro-level photography with this. Landing was just as easy: you descend where you want to land and as you approach the ground the drone halts at about 18ā using its collision detection sensors. With another long hold on the left stick, it initiates the landing sequence and slowly touches down. I also tried the āReturn to Homeā feature, which is enabled as long as you let the drone get a good locked home location before takeoff. Itās so cool to see it work. The drone can be away from you and when you tap Return to Home on the app, the drone comes home and makes a smooth and careful landing. In a couple of tests it came home and landed in a 5-10 foot radius from the takeoff point.
Next is the software. The DJI Go app is what you use when you dock your device with the controller to get the live video, heads-up display, and settings controls, and itās an amazing piece of software. I hadnāt used earlier versions, but in version 4, you can control everything from the app. The video feed from the drone and the HUD view of all the needed metrics looks great (altitude, bearing, distance). Triggers on the sides of the remote snap photos and start recording video. DJI has honed the system down to the simplicity of a video game. Iāve only done a couple of flights, but the video and photo quality is excellent. 4K video from this tiny airframe and camera is a stunning feat.
One of my flights was in about 15 knot winds, and the little guy held up well. The cameraās gimbal was rock steady even in breezy conditions. I noticed a tiny bit of jitter when flying into the teeth of the wind, but not enough to make a difference. I flew one mission of aerial imagery with DroneDeploy, but will dive deeper on that in a future post when I can do more flights.
A few other things on the docket to try:
Object detection and tracking ā you can lock onto a moving object and the drone and camera will follow. When I find a use case for it Iāll try it out and report back. Looks neat from videos Iāve seen.
Flying at high altitude ā so far I havenāt gone above about 150 feet.
Flying at longer ranges ā havenāt yet gone farther than a few hundred yards away, but the range on this thing is huge. When I get more confident with it Iād like to do some longer flights for cool video. Thinking about our Florida Keys trip to Marathon in June!
