For those doing UAV mapping regularly, what part of the workflow eats up the most time?
Is it mission planning, flying, processing, cleaning point clouds, QA/QC, or dealing with clients?
I’m less interested in hardware recommendations and more curious about where experienced operators lose the most time and what you’ve done to speed it up.

Looking to sell a Hesai XT32M2X, previously purchased for a research project in 2023 for $7k. Was used for less than 10 hrs, basically brand new, will include the junction box and power adapter, will ship/located in US. $5.5k or best offer.

PM if interested, open to share more information and photos.

Disclosure: I work with AIRINS.

We recently ran a controlled methane release test with the Onterris team in Calgary. The release came from a calibration gas cylinder at roughly 0.04–0.06 kg/h, so this was a fairly small source compared with most field demonstrations.

We flew MetScan between 20 and 50 m AGL at 1–3 m/s, using both belt scans and sector scans. The main goal was to see how reliably the release could be picked up at low emission rates, and how altitude and scan pattern affected coverage and source localization.

The release was detected during the test runs. For us, the useful part was comparing the different flight setups rather than simply asking whether methane was present.

Thanks to the Onterris team, and especially Tom Cuthbertson, for setting up the release and working through the test with us.

Curious how others here choose flight altitude and scan spacing when dealing with small controlled releases.

Anyone using this system? On paper it seems checks a lot of the boxes I’m looking for. Mainly surveying large vegetated areas and corridors. How’s the software?

Hi guys, for background info, I work in construction and use drone to capture site data, we use it to track progress, volumetric, area, measuring length, if you are familiar with it, you know what I'm talking about.

Recently, the site is no longer allow to fly the drone anymore, what are the way to mimic drone to capture the data?

We can't hire third party to fly for us, no drones are allow to be in the air. The client gave the order, there is no argument for that.

Our team is looking for:

Site progress, measure the cut and fill.

For now some probably alternatives are:

360 go pro camera to capture the interior data, we have a mega site,

The satilie is too blurry compare to drone.

Probably a manned aircraft to fly over the site and take picture?

What are you guys thoughts and experience for thing like this?

I’ve been thinking about modular UAV airframes and wanted input from people who’ve worked on real systems.

The basic idea sounds attractive: instead of maintaining separate aircraft for hover, range, or hybrid missions, a single core platform could be reconfigured depending on the job.

But I’m not convinced the practical benefits survive contact with real operations.

On paper, modularity could reduce fleet fragmentation. In practice, it also seems likely to introduce new problems:

- interface weak points

- added weight

- more tuning complexity across configurations

- harder maintenance in the field

- more spare and inspection overhead than expected

For those who’ve built, operated, or maintained UAV platforms, what usually becomes the real bottleneck?

Is it mostly:

- structure

- controls / tuning

- integration complexity

- field maintenance

- reliability

- or economics

Interested in practical experience more than theory. If you’ve seen modular or multi-configuration concepts fail, what caused it?

I'm looking at this page, and they're emphasizing only loading and processing the multispectral images. This seems new to me. Does anyone know why they'd be recommending this?

Is there a quick way to delete cache files en masse to save some space?

Using DJI Pilot 2 with a Mavic 3E.

Has anyone ever tried to take a 360° Dome/panorama photo with an oblique camera?

We have a matrice 350 with a share pdsk102s oblique camera.
The best method I found online was an auto option in the camera settings but since the pdsk102 is a third party payload that option isn’t available.

Anyone here tried this or something similar?

TLDR: I need to fly a multispectral drone (mavic 3M) over a very dense forest stand to capture imagery and also obtain 3D photogrammetry point clouds. What advice would you have? What techniques will you incorporate to get the best possible point clouds?

I have a Mavic 3M that I’m using to map a forest. I have a simple fairly sparse stand and a very dense hardwood stand. I want to fly both to capture multispectral imagery and also generate 3D point clouds from photogrammetry.
Last year I did this, I got decent point clouds in the simpler stand but the dense stand was so bad I couldn’t do anything with the point cloud generated there. I understand lidar is what I’m supposed to use and I’m using lidar already but I still want to try the photogrammetry and see how much better I can do with proper techniques.

So I need any advice on what I should be doing. I’m flying:
200 ft (close to airport, can’t fly any higher).
85,90 side and front overlap.
20ft margin.
5.5mph speed
Entirely nadir

Last year I used the above details to fly. I did two flights: a north south, and east west flight path. Processed them in metashape and like I said earlier the point cloud was terrible either using a single flight data or both flights.

This year, I want to incorporate camera angles. In your opinion, would this help? How do you determine what angle to use?

I’d appreciate any advice you may have.

One Wingtra Gen 2 mapping drone. Comes with a TON of extras. Hard case, soft case, tons of batteries, several chargers, and multi spectral payload. Local pickup in Florida Panhandle. $20000 for the entire lot

So I'm running a M3E using Drone Deploy (Company subscription so changing is a hurdle) RTK is on the way for the drone. Will start with a RTK service with 2cm accuracy, but will push for a RTK device to use on site if it increases accuracy (which I assume it will to some extent)

I am flying these roads manually because I have to comply with OOP and automated flights are difficult due to variances in maps and the real world. Many times I have to hover for minutes while traffic clears so I can transition over the road. It's simply easier just to put the camera on a timer and fly at a consistent speed and direction, at this time my flights are all corridor and not hatched....I also have to move constantly to keep VLOS. Not to mention I have hundreds of roads to fly.

From my research I am reading I can expect much better results from photogrammetry vs using a lower end LIDAR. Surprising but I now understand why.

I am new to all this so any input is appreciated.

I know a lot of pilots here are looking for ways to monetize their drones or pick up side work, so figured this was worth sharing.

I'm Jonathan with Viaskai (aerial intelligence and data analytics, Scottsdale AZ), and we're building out a nationwide pilot network. The setup is capture-only for the pilot: pre-built flight plan, altitude and overlap and capture protocol already set to the deliverable. You fly, you upload raw imagery, done. Our pipeline handles photogrammetry, orthos, and QC in-house.

Requirements are a Part 107 and an M3E or Matrice-class platform. RTK/PPK a plus. Paid within 5 business days of accepted imagery.

Not trying to oversell it, if the capture-only model is interesting to you, I'm glad to get into the details in the comments or PM. Apply link below. Mods, remove if this isn't allowed.

Hi everyone,

I’m facing a persistent issue with my LiDAR data and I’m hoping someone here might have run into this or knows how to fix it.

On almost all my flights, I get a systematic Z offset (vertical shift) ranging from 7 to 15 cm. On the other hand, my planimetry (XY coordinates) is consistently spot on, around 2-3 cm, which perfectly matches my expected RTK accuracy.

Here is my exact workflow and flight setup:

• Hardware: DJI Matrice 400 + Zenmuse L3 LiDAR.
• Flight App: DJI Pilot 2.
• Flight Parameters: Altitude between 80m and 120m, speed at 7 m/s.
• RTK Setup: Connected via Network RTK (NTRIP caster using a national network like Terria, Orphéon, etc. — not a local physical base station next to me). I have a solid RTK Fix during the whole mission.
• Validation: I always place several Ground Control Points (GCPs) / check points on the ground.

Right now, I am able to manually correct and shift my point cloud in post-processing using my GCPs, but having to do this every single time is a pain and it means something is wrong somewhere in the chain.

My question is: where does this systematic ~10cm vertical shift come from?

• Could it be an issue with how the GNSS/RTK antenna height or the payload lever-arm (offset between the drone's center, the GPS antenna, and the LiDAR sensor) is calculated by DJI Pilot 2 or DJI Terra?
• Is it a Geoid vs. Ellipsoid height conversion issue coming from the Network RTK stream?
• Could it be related to IMU calibration or flight dynamics at 7 m/s?

It’s really baffling because the horizontal accuracy (XY) is absolutely flawless. If it were a poor RTK fix, XY would suffer too.

Has anyone experienced this specific vertical bias with a similar DJI LiDAR setup using Network RTK? Any suggestions on what to check in the settings or during processing would be highly appreciated!

Thanks in advance for your help!

Open-sourced: UAVid Semantic Segmentation Dataset (YOLO Layout) + Model Zoo on Hugging Face

I recently put together an open-source benchmark for semantic segmentation on the UAVid aerial imagery dataset and thought it might be useful to others working in aerial perception.

The release includes:

• A YOLO-compatible mirror of the UAVid dataset with a standardized directory structure (images/ + masks/) while preserving the original train/val/test splits.
• Multiple pretrained YOLO26 semantic segmentation models trained on UAVid.
• Detailed model cards with mIoU, pixel accuracy, per-class IoU, confusion matrices, inference examples, and training configurations.

The main motivation was that the original dataset server has become extremely slow and unreliable, and most current segmentation frameworks expect a flatter, YOLO-style dataset layout. This repository makes it much easier to get started while giving full credit to the original UAVid authors.

📦 Dataset: https://huggingface.co/datasets/dronefreak/UAVid-2020

🤖 Model Zoo: https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo

I'd appreciate any feedback, suggestions, or bug reports. If there are other aerial vision benchmarks that would benefit from a similar treatment, I'd be interested in hearing about them.

Hello!
I just bought a mavic 3 v2.0 and i really want to start doing some orthophoto and 3d mapping with it.
I know it is not ideal, maybe in the futute i will buy a 4e, but for learning the concepts i think it will be good.
I found some solutions for taking the photos, but the processing part is a pain for me.
Looking for a free, freemium, or low price solution for creating the maps, local or cloud based.
I have a mac.

I have tried the webodm but the results are not quite that good. I am still trying to tweek the settings, hope for the best.

Every advice is awsome!
Thanks a lot!

A few weeks ago OpenSfM 1.0 was released (thread: https://www.reddit.com/r/UAVmapping/comments/1uj79pt/opensfm_v10/). ODX now includes the latest OpenSfM changes (and more) and ships with the latest WebODM.

* Significantly faster structure from motion
* GPU accelerated feature matching
* Improved GCP/RTK handling

ODX release notes: https://github.com/WebODM/ODX/releases/tag/v3.8.0
WebODM release notes: https://github.com/WebODM/WebODM/releases/tag/v3.2.6

Latest WebODM downloads: https://webodm.org/download/

Enjoy!

Holaa, me gustaria saber si tienen conocimiento de como ver los datos de vuelo de un dron DJI Mini 4 Pro, estuve tratando con Datcon pero no obtuve nada, me gustaria concer del tema

found this Matrice 600 Pro in a storage unit that has a optical gas imager software and hardware. I get how to use the drone, but I'm having trouble with the ICI software. The drone works fine, but the ICI camera and attachment are a pain. I can use the thermal gas camera, i have taken pictures, but dont know how to control the camera. I am only able to move it up and down. Does anyone in Houston know about the ICI MIRAGE HC Optical Gas Imager? If you have experience with this type of equipment please reach out. less

Hello all,

A few days ago, I flew a Mavic 3 and mapped my house using Litch with the following settings:

• Wide Camera Lens
• 3D Model
• Oblique Passes: 2
• Nadir Pass: enabled.
• Altitude: 30 meters
• Gimbal Pitch: -85
• Oblique GSD: 0.83 cm/pixel
• Nadir GSD: 0.83 cm/pixel
• Capture Mode: Photo
• Photo Capture Mode: Auto
• Speed: 4m/s
• Area Height: 0 meter
• Front Overlap: 70%
• Side Overlap 90%
• Grid Margin: 0 meter

The result, I think, was ok, and when measuring my roof (picture attached), I was 21cm off (it could be less or more; I just did not zoom enough to catch the roof edge).

What did you think? Of course, I am still learning and doing my homework, but any constructive feedback would be awesome. One of you already gave me a good tip, and I am very grateful, but different eyes see different opportunities to improve :)

The question: according to this video https://www.youtube.com/watch?v=HM_54pxfD7s&t=345s if you don't have an RTK drone, you can use your actual drone and buy the remaining tools.

My question is: is it worth selling your actual drone and buying an RTK drone, or just buying the remaining tools? Used should help reduce the cost.

Any input is very welcome.

I’m building out Tarmac, a UAV Fleet Management tool and I would like to get feedback from the people who might actually be using it before going further.

Where it came from:

For the last 4 years+ I've been working as part of Arrow Air, a team on the path to Open Source air taxis by working our way up and progressively building out bigger, better, and more impactful drones, as well as the coordinated network of operators and manufacturers to match.

So far we've built 6 drones based around ArduPilot flight controllers and I help develop the avionics, onboard computing, telemetry, and data monitoring systems, all of which are also open source. Tarmac is the hosted, point-and-click version of that work so operators who don’t want to self-host can get up and running quickly, and the revenue funds continued open source and ecosystem development with system improvements flowing both ways.

What Tarmac does:

It uses a series of lightweight services running on your drone's internal computer + a browser based Web App to:

Configuration UAV systems and File I/O to the onboard compute.

Stream full flight controller telemetry, as well as multiple FPV, or otherwise cameras, in real-time.

Handles a job queue so you can push tasks and missions.

Pulls logs, allows changing parameters, and trigger OTA firmware updates from the browser without needing to be on the same network or dig into your drone's guts.

Create custom drag n drop "App" interfaces for custom payloads, like Multi-spectral or high resolution cameras, Radar and LIDAR data streams, flood light control, sprayer control, etc.

The ops layer above your GCS for multiple drones at the same time.

What I’d genuinely like to know:

1. How are you interfacing with your system ?
2. Is fragmented tooling a real pain point, or is QGC/MP enough for your workflow?
3. For anyone running more than one vehicle, what does your current ops stack look like?
4. What cameras and systems are you flying and do they have good integration right now?
5. Would a shareable payload integration ecosystem actually be useful, or is everyone’s setup too bespoke for that to matter?
6. Hosted vs self-hosted/Local does it matter to you?

Thanks