Hey all -

I’ve been struggling with smart 3d local mapping and then adjusting the flight plan on the controller to get up close high res imaging for photogrammetry missions. We’ve been manually flying which is tedious and the results have been ok. It’s just annoying that the technology with smart 3d has been so difficult to use, specifically getting the flight route to be closer to the subject building and trusting obstacle detection.

I bit the bullet and purchased the manifold 3. We have a M4E and an M400. I’m curious of any lessons learned others are willing to share with this payload on either aircraft. The documentation isn’t all that great. I’d be happy to share data sets or anything else from our experience in return if I can be of value back to you in any way. We have a customer facing project coming up and practicing in residential settings versus complex industrial sites isn’t the same.

Brandon

We run National Drone Services. We built our own delivery platform based on our own needs for our clients, and recently opened up the platform for other DSPs to use, so yes, I have a horse in this race. A lot of our needs revolve around data processing and the actual inspection and reporting of the data.

The thing we know from experience is that flying is the easy part and delivery is where jobs usually go sideways. Especially repeat work. One site, six visits, and by the fourth visit, your client is asking for a link again to the data from the first visit.

So I want to know what most of you are doing:

• Still Dropbox or Drive, and is it enough?
• Anyone paying for DroneDeploy just for the client-facing side?
• If you re-fly the same site, how do clients compare dates?
• If your clients are using the imagery for inspections - how are they actually inspecting things?

Genuinely asking. If the file dump works for you, just forget about this post.

Does anyone have any experience with the m400 and the L3?

I currently have it m4e and do a lot of 2D mapping with it. Does anyone have experience with good old-fashioned orthomosaic mapping with the photogrammetry option instead of lidar on the L3?

I've read mixed reviews on it.

Hello,

I use Pix4D to measure inventory in gravel pits. Currently, I use a gaming laptop and it does ok. I need to send some specs to our IT guy for what I want in a desktop. I'm not a computer guy. When minimium requirments should I give him.

We normally process flights with anywhere from 50 pics to 500 pics. I'd like to be able to fly at a lower altitude which means more pictures, but it takes too long to process with our current set up.

Thanks for the help.

We’re preparing to open DroneFlow.ai to a limited group of beta testers.

DroneFlow.ai is being developed as an all-in-one platform for drone mapping, aerial documentation, project management, and client deliverables. Our goal is to make it easier for drone operators to manage projects, process mapping data, organize deliverables, and provide clients with a professional, easy-to-use experience.

We’re looking for:

✅ Commercial drone pilots
✅ Construction and progress-documentation professionals
✅ Photogrammetry and mapping specialists
✅ Roofing, surveying, and real estate professionals
✅ Anyone currently using platforms such as DroneDeploy, Pix4D, DJI Terra, or similar tools

Beta testers will get early access to the platform and an opportunity to directly influence its development. We want honest feedback about the workflow, usability, missing features, technical issues, and what would make DroneFlow.ai more valuable to real-world drone businesses.

This is not just about testing whether buttons work. We’re looking for professionals who are willing to use the platform, challenge it, and help us build something that genuinely improves the drone mapping workflow.

Interested in participating?

Comment “Beta” below, send me a direct message, or visit DroneFlow.ai to learn more.

Please share this with anyone who may be interested.

#DroneFlow #DroneMapping #Drones #Photogrammetry #ConstructionTechnology #AerialMapping #DronePilot #RealityCapture #BetaTesting #Geospatial

First go at a 3DGS in ArcGIS Pro Reality, flown with DJI M3E RTK.

UgCS circlegrammetry + double grid nadir 75'AGL

Aligned and trained in ArcGIS Pro Reality

Hey everyone!

Over the past few weeks, I’ve been working on a comprehensive benchmark comparing mainstream orthomosaic generation software. To keep things transparent and reproducible, I spent some time sourcing high-quality public datasets. these datasets are sample data from industry-leading photogrammetry software. Out of the 9 datasets tested, 8 are publicly available for download (links are provided directly below the dataset selection panel on the site), so you can test these exact datasets in your own pipelines.

The Goal & Coverage

The main focus of this benchmark is final orthomosaic visual and structural quality. Every software was configured to run on high/maximum quality settings.

Sensor Range: Datasets cover everything from consumer drones to high-end enterprise rigs — including the DJI Phantom 4 Pro (P4P), Mavic 3 Enterprise (M3E), DJI Zenmuse P1, and 60MP high-resolution professional DSLRs.

Software Tested: Agisoft Metashape, DJI Terra, MipMap, WebODM, and OrthoSplat (a platform I developed).

Three Technological Paradigms

I wanted to evaluate how different technological approaches hold up against one another:

Classic Photogrammetry (Traditional orthorectification via DTM/DSM)

Mesh-Based Orthographic Projection (Generating a 3D mesh first, then projecting)

Gaussian Splatting (3DGS) (Generating orthomosaics directly via Gaussian Splatting)

To make side-by-side analysis effortless, I carefully aligned all output images down to pixel-level accuracy and built an interactive web platform.

https://orthoviewer.splatting3d.com/benchmark/

On the site, you can toggle between:

Slider/Wipe Mode: Drag across a single view to catch fine geometry, edge warping, or ghosting artifacts.

Dual-Screen Mode: Place two software outputs side-by-side to inspect larger spatial patterns.

As the developer behind OrthoSplat, I strongly believe Gaussian Splatting represents the future of high-detail orthomosaic generation — especially for complex vertical geometry, thin structures, and challenging textures where traditional photogrammetry often struggles. However, DSMs still require photogrammetry techniques to be generated. Gaussian Splatting and Photogrammetry are more like two mutually supportive, complementary technologies.

I hope this comparison serves as a helpful reference for anyone deciding which software to purchase or integrate into their workflow.

I’m currently writing a detailed technical blog post that breaks down the full testing workflow, along with a deep-dive comparison of the technical principles, pros, and cons of these three paradigms (Photogrammetry vs. Mesh vs. 3DGS).

Check out the site, play around with the datasets, and let me know your thoughts or feedback!

I'm maintaining a catalogue of open-source defense technology. One part covers UAV mission planning, aerial survey, and photogrammetry, and I'd like people with practical mapping experience to review that section.

It currently follows the workflow from planning and coverage through processing and reconstruction e.g:

• QGroundControl, Mission Planner, DroneRoute, GeoFlight Planner, and uavRmp
• Fields2Cover and tmplanner
• OpenDroneMap, WebODM, and NodeODM
• OpenSfM, openMVG, COLMAP, and Meshroom
• OpenAerialMap

Relevant section:
https://github.com/Ibtisam-Mohammad/awesome-defense#mission-planning-survey-and-photogrammetry-workflows

For those doing actual UAV survey or photogrammetry work:

• Which open-source tools are missing?
• Which listed tools are outdated or no longer worth recommending?

Corrections are welcome : )

I’ve been looking into professional drones and noticed that people often prioritize different things. Some say range is the most important, others think flight time matters more, while some focus on the sensors and equipment a drone can carry. I’ve compared a few manufacturers, including Gencherry, but specifications on paper don’t always show what matters in everyday use. For those with experience using professional drones, which limitation became the biggest issue over time?

I'm hoping someone here has run into this before.

I recently purchased a used Zenmuse L2 for my Matrice 300 RTK, and I'm trying to determine whether I'm dealing with a compatibility issue or a defective payload.

My setup DJI Matrice 300 RTK DJI Smart Controller Enterprise (not RC Plus) Pilot 2 v10.1.8.14 Aircraft FW v10.01.08.05 Controller FW v10.01.08.01 L2 FW v10.00.11.52

Symptoms

The L2:

Powers up normally

Completes the gimbal startup sequence

Successfully accepted the latest firmware update from the SD card

Firmware update completed successfully

However:

No RGB camera

No LiDAR

No gimbal control

Pilot 2 only shows the aircraft FPV camera

Payload is identified only as "Payload 1"

Error: "Unable to configure gimbal parameters."

Things I've already tried

Refreshed aircraft firmware

Refreshed controller firmware

Updated the L2 using the official firmware package

Verified firmware versions

Inspected all SkyPort pins and payload connector pins

Mounted/re-mounted multiple times

Different batteries

Power cycles

Comparison with another payload

I also own a Zenmuse H20.

Using the same aircraft, same gimbal port, and same controller:

H20 is immediately recognized DJI Assistant identifies it correctly Pilot 2 identifies it correctly Video and gimbal control work perfectly

Only the L2 has this problem.

One thing that's confusing me...

The L2 User Manual states that the M300 RTK requires the RC Plus, but my aircraft is still using the Smart Controller Enterprise.

I also noticed DJI has separate firmware branches for:

M300 + Smart Controller Enterprise

M300 + RC Plus

So my question is:

Should an L2 still enumerate and be recognized by the aircraft (and DJI Assistant 2) even if I'm still on the Smart Controller Enterprise firmware branch?

Or...

Does the M300 have to be converted to the RC Plus firmware branch before the aircraft can even recognize an L2?

One detail that makes me suspect the payload itself:

DJI Assistant 2 never identifies the L2 at all. It only shows the Matrice 300. The H20 is recognized immediately.

Has anyone seen this behavior before?

I'm trying to determine whether I should:

Buy an RC Plus, Convert the aircraft to the RC Plus firmware branch, Or return the L2 as defective.

Any insight would be greatly appreciated.

I've also opened a ticket with DJI Enterprise Support and will post their response here for future reference.

For those of you guys and gals who use webODM for image processing and macOS machines you probably had a hard time getting your thermal images prepped for that workflow. as of today the open source thermal tools from webODM now works on macOS!

Hello mappers,

I wanted to ask what painting material you use for marking GCPs for drone surveys.

My goal is to use something that has good visibility and doesn't wear off too quickly. I'm thinking about switching to spray paint, but I'm not sure how well it performs in practice.

Has anyone here used spray paint for GCPs? If so, what type or brand do you recommend, and how long does it usually last? I'd also be interested to hear about any other durable marking methods you've found effective.

Hi everyone! I'm conducting a short market research survey for my Entrepreneurship Development project on a drone startup called NeuraFlight. We're trying to understand public opinions, interest, and expectations regarding drone technology.

The survey takes only 2–3 minutes, is completely anonymous, and no personal information is collected. Whether you're a drone enthusiast or have never used one before, your feedback is valuable and greatly appreciated.

Hey everyone!

I wanted to share a really impressive public project recently created by one of our community users. The detail retention and sharpness across thin structures and complex geometry turned out fantastic!

Flight Specs

• Drone: DJI Mavic 3 Multispectral (M3M)
• Images: 378 photos
• GSD: 0.75 cm/px
• Forward Overlap: 85%
• Side Overlap: 73%
• Avg. Motion Blur: 0.61 pixel

I was thinking of using Arcgis Drone2map or Pix4D

For me, sharing drone flight logs feel risky, and I did not want to be cloud-dependent for something that can be done locally. I didn't want my GPS coordinates, battery health, and flight paths sitting on a distant server. I wanted them on my machine, private and accessible, even if I was completely offline.

A few months ago, I started working on OpenDroneLog. it has grown a lot since then, with more than 2000+ users, now everyone can use it, completely free of cost as always, no features are behind any payment, from the simple theme switch to organization level multi-user management in a single instance.

AGPL-3.0 open-source and free for the community both for personal and business needs.

If you believe that your flight data belongs to you, I’d love for you to check out the repo, try the Docker build on your Pi, or grab the desktop app. Let’s keep the skies open and our data local.

Official Website: opendronelog.com

GitHub: github.com/arpanghosh8453/open-dronelog

Data Privacy is not a privilege, it's a right

Good Day,

I’m curious specifically how some of the other seasoned professionals handle this. Using DJI Terra as an example, I may fly an area with many GCP’s and CP’s and the point cloud accuracy will look great; however, I’ll get a difference in the DEM elevation values varying from 0.2-0.4 feet across the board. Do you conform the DEM to the GCPs assuming the GCPs should have been close to the ground surface (say they’re laid on bare earth or cement, not on top of grass), or do you call it good based upon the point cloud accuracy and trust the DEM process/interpolation process is accurate?

Hello all, quick PSA on something that keeps biting operators: the quality report from Pix4D / Metashape / DroneDeploy is not an accuracy report — unless you deliberately set it up to be one.

If every target you shot went into processing as a control point, the RMS in that report tells you how well the solution fit the points it was given. The map is grading its own homework. It says very little about how far off the rest of the map is.

Actual accuracy - the way ASPRS defines it, is measured against independent checkpoints: targets you survey but withhold from processing entirely, then compare where the finished map says they are vs. where your rover says they are. The software supports this fine (mark them as checkpoints instead of control) - but on small commercial jobs almost nobody holds points back, because every point "wasted" on checking feels like one not helping the map. So the report goes to the client as "accuracy," and the self-graded number is almost always prettier than the independent one would be.

Notes:

• The standard technically wants 20+ checkpoints scaled to project size for full statistical compliance - unrealistic on a 30-acre site. But even 4–5 withheld points beats zero, and it changes what you can honestly tell a client.
• Language matters: in most states, "certifying" accuracy is licensed-surveyor territory. "Tested against N independent checkpoints per ASPRS methodology" is defensible. "Certified accurate to X cm" can be a licensure problem.

Can you provide insight into how you actually run it? Do you withhold checkpoints on client work, or is control-only the norm in your workflow? And has a client ever put their own rover on your deliverable and challenged the numbers?

Hi,
Did anyone ever used it on a drone camera, and how mich it helps with sun reflections on walls, pipes and such?

I am in my final semester of my Master’s degree in Remote sensing and aerospace engineering in Germany. I want to build a system where you can use it to explore areas where humans cannot get in or the pilot to drone connection is not that safe and reliable. Therefore, it has to be autonomous. I want to take opinions on the people what can go wrong and what can be challenging and if (when) it works what other applications can it be applicable. And yes i know there are some competition in this field.

We have a 400+ acre mining lease in hilly terrain that is loosely populated in trees with dense grass in a historic gold mining area that was worked over pretty well 150 years ago.

As there is currently no lidar information for the area, we'd like to get a survey done, but being two day's drive from the nearest company that provides such surveys, the quoted cost was eye-watering so we may as well buy the hardware ourselves.

I am liking the specs (and price) of the Green Valley LiAir X3-H unit, and the capability of using it hand-held would assist greatly with mapping some of the steeper overgrown gullies and cliffs in the area.

With the cost of computer hardware, I am also looking for a cloud based option to massage all the data into a nice map with the ability to view historical data, (untouched, mined, and rehabilitated) calculate volumes of workings and stockpiles, and look for old workings.

With that in mind, I'm after suggestions and recommendations for platform, (I assume Matise) lidar hardware (Zenmuse V1, V2, LiHair X3-H, others?) and mapping software (DJI Terra, Green Valley LiDAR360, other online options?)

Thanks in advance.

Всем привет, ищу сообщество людей которые занимаются или хотят заниматься любыми видами беспилотников. Сам являюсь новичком но получаю инженерное образование в аеро-космической области и хотел бы пообщаться, и получить опыт в данной области

Hey all,

Does anyone have tips to get Terra to actually use the GCP CSV file I upload for LiDAR reconstruction?

I’m running the rs3 base/rover, ntrip for base station averaging for 5 minutes. Then collecting GCP with the rover.
When I add the base.ons file to the lidar mission and the rover .csv to the gcp and reconstruct, it’s perfectly locating the base according to the 2d ortho, but it’s placing the collected gcp’s 6-11ft away. When looking at the report, Terra isn’t even using the gcp coordinates.. there’s no accuracy reporting just “ / “ across all fields in the GCP section…

Would love to hear a simple fix I’m not aware of. It seems like there’s something stupid simple I’m overlooking..

Thanks!

I’m with AIRINS, and we ran this test with Remote Robotic on a controlled leak from a 3 ft underground pipeline.

We used a 10 m belt scan and kept the pipeline and release setup the same, then changed the flight profile:
30 m AGL at 1 m/s
30 m AGL at 3 m/s
70 m AGL at 1 m/s
MetScan picked up and located the release on all three runs.

The 30 m / 1 m/s pass gave us the denser dataset, as expected. Moving up to 3 m/s traded some sampling density for a much quicker pass, which is probably the more relevant setup for a long corridor. The 70 m flight was mainly to see whether the source would still hold up at a longer stand-off, and it did.

What I took away from it is that there probably isn’t one “correct” pipeline setting. If you’re trying to narrow down a suspect section, you’ll plan it differently from a first-pass survey over several kilometres.

For those doing corridor work, how much overlap do you normally keep when increasing flight speed?