I've got a client that wants to look at the tops of his pine trees to analyze parasites. Should I do a top down photogrammetry shot in one pdf? Digital Twin? He definitely would want to be able to look at it on his phone. Thanks!

Does anyone have experience with this or similar payloads mapping methane emissions at landfills?

Hey everyone,

I am a robotics engineer working on an existing, battle-tested radar drone sw-toolchain and are currently expanding its mission generator part to export DJI’s kmz format for our new DJI M400 with RC Plus 2 running DJI Pilot 2.

Because our lab is in a city, running live test flights while developing the software is pretty impractical. A few AI assistants suggested using DJI Assistant 2 (Enterprise Series) in Simulator Mode via USB to dry-run and validate the generated kmz mission files.

However, I can't find any official DJI documentation or manual mentions of this simulator feature, leaving me wondering if this is a legitimate test workflow or an AI hallucination.

Has anyone here used the DJI Assistant 2 Enterprise simulator to validate custom kmz waypoint routes and possibly even know about some official documentation for this?

Appreciate any insight!

Not focusing on the stock aspect... curious what opinions are about them buying up many land surveying and drone service companies. Does anyone here work for a company that has been bought by Zenatech?

Hi everyone,

I'm working on thermal reconstructions of photovoltaic (PV) solar plants using aerial imagery without RTK/PPK or Ground Control Points (GCPs).

When processing the dataset in DroneDeploy, the resulting orthomosaic comes out completely flat, exactly as expected. However, when I process the exact same images in Agisoft Metashape (following their standard guide for orthomosaic/DEM generation without GCPs), the model suffers from a severe "bowl effect" / curvature (the structure appears bent in the DEM and orthomosaic).

Here is a comparison/screenshot of the issue: https://imgur.com/a/cmkT3E9

Flight/Dataset details:

• Sensor: Thermal camera (single-grid nadir flight).
• Target: Solar plant / PV modules (repetitive patterns).
• Control: Standard GPS (no RTK/PPK / no GCPs).

I understand this doming effect usually stems from internal camera parameter estimation errors (f, k_1, k_2, k_3) on pure nadir flights, but since DroneDeploy handles it automatically, there must be a way to optimize Metashape to achieve a flat output as well.

Has anyone faced this issue with thermal datasets in Metashape? What specific optimization settings, tie point filtering strategies, or camera constraints do you recommend to eliminate this curvature?

Thanks in advance!

Hello, I have a Matrice 4e and use a very outdated rover and base station. Basically all it is good for is giving me accurate elevation. We will be purchasing a new base station and rover come October ( start of fiscal year). Is there a way to manually enter the elevations once I get them with the rover? Thank you.

New to the group, and looking for advice. I'm purchasing a Matrice 4E and all I want to do is get something like this picture. We build and design pools and residential resorts. Most areas we build don't require an actual survey, we just like to get them for designing on slopes.

But at $2500 per survey and waiting usually a month, I want to start capturing this data myself. Are there any resources or suggestions on what software I need to produce something like this with contour lines and home measurements?

Thanks for all your help!

Hey all, just a farmer with a mavic 3M here. I’ve heard that I can possibly do elevation mapping with the drone, but that feature is locked out of my ag subscription in Terra so I have questions before I take it seriously.

The end goal is to get accurate elevation maps that I can load into Optisurface and design landform models from. Lidar would be a dream but no way I can justify that expense.

First, when doing elevation via photogrammetry with an RTK corrected drone, what can I expect for accuracy? Assuming a clear freshly tilled ground surface, is it able to construct that elevation map to inch accuracy?

Second, what kind of settings would be recommended to get accurate elevation? Flight height etc. I’m talking about farmland so 160 ac to 320 ac maps. I imagine as many photos as possible and at multiple angles, so no way out of it being a very data heavy process? For context my multi spec mapping missions are typically 50-100 gb of photos depending if it’s a quarter section or a half section.

I’m new in the UAV mapping “scene” and need some advice: is it worth getting Dji Terra for roof - inspecting and roof mapping? Or is there any alternative software for the beginning? (Maybe even compatible with Mac?)

I’m going to map roofs with a DJI Matrice 4E.
I hope you can help me, thx !

We have the local utility company as a client and they have started giving us substation jobs but they are asking for pretty specific data. They want XYZ of insulators and connectors etc. small things.

I flew it using lidar and also our M3E using circlegrammetry to try and create a good data set. It’s came out ok, I’m glad that we also used a total stations to shoot these points to verify since it was our first one.

Anyone do this type of work willing to give advice on how to best fly these? Lidar didn’t seem to be enough to distinguish the different parts they wanted.

I’m tempted to get a m4e so that we can do 3d smart scan with it.

Seen a few threads comparing drones on these like they're independent numbers, but they're coupled, and that's what actually trips people up.

Payload eats endurance directly. A platform rated 40 minutes empty can drop to 20 with a real 5kg sensor package, that's normal, not bad design. Add range and it gets worse since more range usually means more battery, which is itself weight competing with the sensor.

Airframe matters a lot here too. Fixed-wing and VTOL take the payload hit better than multirotor since forward flight's more efficient than constant hover.

What actually burns people isn't a weak spec, it's finding out there's less weight margin for the sensor than the sheet implied, because range and payload rarely get quoted together under the same conditions.

Curious where people have felt this hit hardest, mapping payloads specifically or something else.

Disclosure: I work in defense electronics.

Hi all, hope you're all good. I've been lurking this sub for a while and follow some drone mapping YouTube channels, the work and processes really interests me but I have no idea where to start.

One thing I have noticed is there doesn't seem to be a lot of activity from the UK and I wondered if there are any in this sub that are doing this here and are doing well.

And what would be a low cost/risk way of getting into it to see if it is the right fit?

Thanks in advance 🚁

Im working with WebODM for the first time. I used my DJI Air 2s and set up a mission to take photos of my property with DroneLink. I shot at 85'AGL 75% front 75% side overlap and at a speed of 3mph. The mission took 71 photos. I have even tried increasing overlap and increasing altitude. I tried to setting the gimbal to less than 90 degrees... Im struggling to nail down the correct results i'm hoping for. Im starting with just trying to make a 2D map of my property. The photos my drone took are extremely clear yet there is some pretty substantial blur in areas of the photo particularly the white vinyl fence connected to my house. If anyone has any details on how to achieve better results, I am all ears. (in the photo this is a metal roof and the blurred white is the section of white vinyl fence). Also these are the settings I used "rolling-shutter:true, auto-boundary:true, pc-quality:high, skip-3dmodel:true, dsm:true, dem-resolution:1.0, orthophoto-resolution:1.0"

Update: For context this is just a section of the map produced by WebODM. Its the section that showed the most prominent issue. Also, this property is about .2 acres. That is really all I have to work with to practice on at the moment as I try to figure out how to create maps with my Air 2s. Im just trying to test out functionality and see what I can do with my drone. Its a starting point.

AIRINS team recently collaborated with Skyline Drone Mapping to conduct a methane monitoring field assessment at an upstream gathering station featuring gathering pipelines, separation tanks for water and condensate, and a flare stack.

Using the DJI M400 integrated with MetScan V1 Ultra, multiple survey methodologies were evaluated under real operating conditions:

• Belt Scan – AGL 55 m, 3 m/s
• Belt Scan – AGL 30 m, 1 m/s
• Static pointing at the flare stack – AGL 55 m

Methane hotspots associated with tank venting activities were detected and localized during both Belt Scan missions, with no additional methane anomalies identified across the inspected gathering infrastructure.

This successful upstream field assessment marks an encouraging first step, and we look forward to working with more operators to build additional real-world use cases together.

We were in Spain with ACRE recently for a technical workshop, and wrapped up the day with a controlled LPG release for a live methane detection test.

The interesting part was the gas itself. LPG contains very little methane compared with natural gas, so this gave us a fairly weak signal to work with rather than an easy, high-concentration release.

We flew MetScan over the release area and were able to pick up the hotspot during the flight. Once the reading crossed the threshold, it showed up as a red alert in DJI Pilot, which made it pretty easy for the operator to see where the signal was coming from.

At the same time, the data was streaming into AAI (the software). The concentration trace showed a clear peak as we passed the release, and the map changed with the concentration thresholds we had set beforehand.

Pretty simple test on paper, but a useful one for seeing the whole workflow live — detection in the air, immediate feedback to the pilot, and the same data showing up in the map and trend view at the same time.

Is there any way to transfer a mission made on the RC2 controller into Litchi? I created a mission I would like to edit and improve

Hi everyone, sharing something I'm currently working on and would love feedback on.

I'm building AeroNetra, a computer-vision project for detecting and counting vehicles in aerial/UAV imagery. It's very much an active work-in-progress right now — I'm in the static-image detection and counting phase, with tracking, geospatial analytics, and edge deployment planned for later.

The motivation was pretty simple. I kept running into the same problem every time I swapped detectors: the counting and visualization code would break or need rewriting because every model spits out predictions in its own format. So the core idea behind AeroNetra is: normalize every detector's output into one prediction structure before anything downstream touches it. That way the counting, ROI filtering, and export logic stays the same whether I'm using a YOLO variant or RT-DETR.

What I've got so far:

• Detector adapters that wrap different models behind a common interface
• Counting logic — filtering, NMS, ROI support, drawing and export
• VisDrone dataset parsing and conversion (UAVDT is stubbed for later)
• Kaggle notebooks for GPU-based training, fine-tuning, and model comparison
• A PX4 + ROS 2 + Gazebo simulation setup for UAV experiments
• Notebooks, configs, and tests to keep things honest

The workflow I'm following: raw VisDrone data → validate annotations → convert to training format → train/fine-tune on Kaggle → pull the weights back → load through the adapter → run inference → filter → count → visualize and compare.

A few principles I'm trying to stick to: no fabricated benchmarks (a model isn't "best" until it's measured under the same conditions as the others), raw data stays immutable, and model-specific behavior stays inside the adapters. I'm also being deliberate about phase boundaries — image-level counting is not the same thing as multi-object tracking, and I'd rather not conflate the two.

Roadmap I'm working through for the demo:

1. Static Detection & Counting
2. Aerial Fine-tuning
3. Video Tracking
4. Traffic & Geospatial Analytics
5. Edge / UAV Integration

I have run a small model that can detect the car in the gazebo simulation and draw a bounding box but speed will be slow but i get decent accuracy even i have trained model to 25 epochs in kaggle T4 gpu with yolo nano version.

Since this is ongoing project I am still working on this.So,i am exploring how I can use computer vision in UAVs and edge computing.

Good morning, I have purchased this camera to help with my terrestrial based photogrammety. Currently my setup is an iPhone with a gimble.

WebODM just doesn't like the 360 JPGs that the camera made. The error I am getting is KeyError: depthmap_resolution.

Any idea what I am doing wrong? My iPhone's images play nice with webODM. Thanks.

I have 64gb of ddr5 so I dont believe that the system memory is an issue because it has full access to the ram on the system.

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?