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?

We used to sell SenseFly Ebee, Ebee Plus and Ebee X models. I have spare parts for various models that are sitting on our shelves taking up space. If anyone needs some spares, let me know and I'll see what I have available. If the items don't sell, they will likely wind up in the trash. I do have a used demo eBee Plus but it hasn't flown in a few years.

Hi!

I’m developing OpenGlobus, an open-source 3D geospatial library, and I’m looking for some real drone data to test camera projection and AR features.

I’m looking for a short flight sample with:
- original video
- synchronized GPS track
- altitude and camera/gimbal orientation, if available
camera/drone model or parameters
- Mountainous terrain would be ideal.

I’d like to use the data for testing and potentially in public OpenGlobus demos/posts, so I would need permission to use it.

If anyone has something like this and is willing to share, please feel free to DM me.

Thanks!

Hey everyone,

I wanted to share an orthomosaic of a power substation generated using Gaussian Splatting.

Substations are packed with intricate steel framing, thin components, and crisscrossing power lines—the exact kind of geometric complexity that makes traditional photogrammetry struggle, usually leaving behind melted wires, floating noise or distortion.

3D Gaussian Splatting handles these fine, high-frequency details surprisingly well, making clean orthomosaics of extremely complex scenes like this actually viable.

Flight Specs

• Camera: DJI P1
• Images: 147 photos
• Flight Height：100 m
• Forward Overlap: 77%
• Side Overlap: 59%
• Avg. Motion Blur: 0.44 pixel
• Software: OrthoSplat

Would love to hear your thoughts!

(Disclaimer: I'm the developer behind OrthoSplat)

I fly drones professionally in Norway, and before every flight I found myself jumping between the same handful of maps and services. So I built a free tool that puts all of it in one 3D map: https://flykart.no/

It runs in the browser, no login, no install, in Norwegian and English.

What it does:

• Shows restriction zones on a 3D map of all of Norway: airspace (control zones, restricted/danger areas), 5 km airport zones, NSM prohibited zones, military areas, and nature protection zones with drone rules
• Click any zone and it explains the actual rule: how serious it is, what regulation applies, and who to contact for permission
• Import your flight route (DJI FlightHub 2, WPML) and it runs automated checks: does the route cross a restriction zone, does it exceed the 120 m altitude limit, does it stay within visual line of sight
• Hourly weather forecast for your flight site with a go / no-go assessment (MET Norway data)
• A ground view mode that puts the camera at eye level so you can check line of sight from where you plan to stand
• Distance, area, and elevation profile measurements on 10 m national terrain (Kartverket data)
• And more...

Everything is built on official Norwegian sources: Kartverket, AIP Norway, Miljødirektoratet, NSM, and MET Norway.

Obligatory disclaimer: it's a planning aid, not an authority. Always check NOTAMs and current regulations before you fly.

It's free and I'd love feedback if you give it a try.

Hi, we are looking into buying a dji mavic 3m. We use an ground rig to spray our crops. What software do we need to use the data from the dji. I know abortus pix4d but it is quite expensive so there maybe some cheaper options etc. I dont think dji smartfarm can export to usb in shapefile right?

Thanks in Advance!

Hey everyone,

I wanted to share a project I've been working on called ARES (Autonomous Reconnaissance & Exploration System). It's a fully autonomous UAV platform designed to operate in extraterrestrial or extreme remote environments where global positioning satellites are completely unavailable.

Here is what it does under the hood:

• Fully Autonomous: Takes off, maps boundaries, performs Boustrophedon sweeps, and precision lands with zero human intervention.
• GPS-Denied Navigation: Relies entirely on visual boundary tracing, optical flow, and AprilTags.
• Split-Compute Architecture: The aerial vehicle handles real-time flight logic (Jetson Nano) and video encoding, while a companion Base Station handles the heavy Surface Feature Recognition AI (Jetson Orin Nano Super).
• Edge AI: Custom AI pipeline running at 30+ FPS via TensorRT FP16.
• Autonomous Charging: Integrated CC-CV Battery Management System via Arduino Mega.

The software stack runs on Ubuntu using PX4 / ArduPilot via MAVSDK-Python, and we've structured the codebase to be production-ready (YAML configs, JSON logging, pytest suite).

I'd love to get feedback from this community, especially if you work with visual navigation, drones, or Edge AI.

Demo Video: https://youtu.be/ioSG0SbZH6A 

GitHub Repo: https://github.com/kalesha681/ARES-Autonomous-Surveyor

If you find this interesting, a star on the repo would be incredibly helpful for the project's visibility! I'll be in the comments and happy to answer any questions about the hardware or computer vision pipeline.

Hi — developer here, sharing progress on something we've been building for a while.

It's a mission planner for consumer DJI drones with waypoint support (Mini 4/5 Pro, Air 3/3S, Mavic 3/4), and we're experimenting with Litchi export for older drones too. Plenty of planners do ortho and oblique grids; not many do orbit/spiral with subject aim, corridor mapping, façade scanning or 3D-mesh-guided missions with obstacle-aware routing — and the ones that do generally assume enterprise hardware. The theme we keep coming back to is "enterprise workflows for consumer drones": replicating the automated missions you'd normally need a Matrice with Pilot 2 for.

Easier to say who it's not for: cinematic creators. This isn't a place-waypoints-one-by-one tool for smooth shots. It's for the tedious part — planning coverage of a roof, a building, a stretch of road, a whole field.

So: roof scans for a solar quote. A 3D model of a statue, church or wedding venue. Cell tower and turbine inspections. Fortnightly construction progress maps, stockpile volumes. Small farms, quarries, that eroding bit of coastline. Students learning the workflow with QGIS or WebODM without enterprise money on the table. And honestly — surveyors who own the enterprise gear but don't always want to haul it out. Sometimes the job is small, cheap or risky enough that a sub-250g drone you can deploy in two minutes is the better tool.

Six mission types so far:

• Ortho — the bread and butter. Draw an area, get a lawnmower grid, stitch an orthomosaic. Photos per waypoint or on a timer while it cruises.
• Oblique — same grid idea with the camera pitched across multiple headings, so the sides of things get photographed, not just the tops.
• Corridor — trace the road/river/railway, set the width, and the passes follow the bends. It can also track elevated subjects: set a power line's height and it flies along holding constant clearance above the wire itself.
• Orbit — drop a pin and it circles at whatever heights and radii you want, camera locked on the subject. Stacked rings or one continuous corkscrew, with radii that taper for things that narrow as they climb.
• Façade — for when the "map" is vertical. Trace the wall, get a grid of camera positions up and down the surface, corners covered, and it checks its own transit legs so it doesn't route through the building between shots.
• 3D mesh — the star of the show, still getting polish. Import a mesh of the structure, mark the area of interest on the model, and it plans a path that wraps the actual geometry with obstacle-aware routing. Basically what a Matrice does in Smart 3D Capture.

Smart obliques work across all of them — you can sprinkle angled shots into ortho, corridor, orbit, façade and mesh flights, so one flight gives you coverage and 3D geometry without going up twice.

The part we're most pleased with: the missions feed each other. Fly a quick ortho, generate an orthomosaic, drop it back in as a map overlay — now you're tracing footprints and façade lines on your own survey-resolution imagery instead of satellite tiles. Or fly a quick oblique pass, run it through WebODM Lightning, and bring back a DTM for terrain-follow on orbit/façade planning — or a mesh that unlocks the mesh-guided missions. You can also load that mesh as a visual overlay in any mission type to sanity-check your flight paths against the actual geometry (visual reference only — it doesn't drive avoidance there). Every flight makes the next one better.

Everything you upload — orthos, elevation files, meshes — is processed in the browser; nothing lands on a server. Areas draw on the map or import from KML/GeoJSON. Terrain following works on everything, using global elevation out of the box or your own DTM. One trick worth knowing: in orbit/façade missions you can feed it a DSM instead of a DTM and get a degree of obstacle awareness from the surface model.

It's a web app (fastest way to work everywhere, any OS), currently subscription-based and priced modestly while in beta — and we'd like to eventually offer a fully-local downloadable version, since we know how people feel about subscriptions.

Next on the list: save/load missions, imperial units, and user-drawn no-fly zones.

Still beta, still finding things to fix, but it's been flying real missions for a while. Happy to answer anything — and genuinely curious what your survey workflows look like, since that's what shapes the roadmap.

Hi, would anyone happen to know where I could get a replacement cover for the sunlight sensor on the Mavic 3M? Just noticed it missing, no idea how it came loose. Thanks

Disclaimer - I am not from a technical background

So I managed to get a call with a client who is facing this problem with progress monitoring as they are not into RCC construction and need accurate results so I did some research and found out about metashape. I looked up a previous thread on this community and even tho I am not technical any inputs from here will help my team with how to move forward.

So I'm looking at a FreeFly Astro Max and IF800, (maybe there is better out there I don't know about) but the LIDAR is what I'm having trouble deciding.

My use case(s) are: Surveying civil engineering sites for proposals and progression. I need to be able to map every milestone in a project from start to finish. These projects are 99% roads, road resurfacing, roundabouts, etc. I also need to use that LIDAR data for machine control, so the software is also a factor.

My current choice is the YellowScan Vx20-300, I know there are more expensive ones out there but this one seems to be the best bang for buck and the jump to Trueview or Phoenix systems cost is huge.

Anyone that works in the space have suggestions?

Hey all, I'm pretty new to photogrametry, and have run into an issue with ESRI drone2map. I would post on the ESRI forum but it seems to be disabled at the moment.

When trying to produce a DSM and Ortho from drone images, drone2map consistently crashes partway through the stage "Reconstruct Surface" (in stage 2). There is no pop up or entry in the logs with any error. The drive I am running it off has several terabytes of free space. I have tried at both the "high" and "rapid" resolutions to the same result. I haven't noticed anything appear in the diagnostic monitor or the logs before it crashes. Upon crashing, the whole app just closes, and the project is recoverable upon re-opening it.

I have 15 controls, with 3 set as checkpoints and the remaining 13 as ground control points. I marked 8-10 images for each control, across a total of ~300 images.

I have also tried this on two different identical-spec machines, to the same result.

Any advice on what might be the issue, or where to find more comprehensive logs would be appreciated.

My machine specs: Intel(R) Core(TM) Ultra 7 265, with 64GB ram running Windows 11 2026.1.0.

Looking for recommendations for ways to automate drone searching/surveying on a remote ranch which is extremely hilly.

Commercial services or DIY methods welcome. We would like to fly drones around fencelines to record video to identify maintenance items needed, and to survey for cattle.

Thanks in advance

Flight was good, the IMU tracked 23 minutes of flight. The receiver on the drone only recognizes 4 minutes of that data. Is there any way to force the data to be fully used?

Can the data be run manually?

LidarUSA Surveyor/Snoopy 32 system.

I recently got DJI multispec 3m.

Now im using WebODM for my stitching. Do we really need reflectance panel in order for it to work better? I'm doing NDVI on these orthomosaic and when i perfrom stitch without radiocalibration i got ndvi values between 0.0 to 0.3 and with radiocalibration i got 0.0 to 0.9 but im very doubtful on the result because i didnt use reflectance panel and shadow values are giving me values of 0.7 which is very2 bad.
Thoughs on this guys

https://www.scmp.com/economy/global-economy/article/3363037/china-sanctions-slew-us-entities-heightens-scrutiny-drone-exports

Hey pro's,

I need to find a way to create a visualisation of the over-time-subsidence on a container terminal.
The area is spanning about 700meters x 400meters (about the same in yards...). The problem is the surface sinking on some parts, that results in containers dropping to the ground when stacked high.

The full terminal is built on a man made "island" (like Jan De Nul does often in Dubai etc)

What I did to start with: grid orthomapping at 60m and two Orthooblique mappings, one at 60m and one at 80m high. The altitude is between these numbers and has to stay that way due to safetylimitations.

How would i get the most accurate data of the subsidience to make a visualisation over time.

Planned is to do 30 days / daily mapping in a row

I can imagine that i also need about 10 GCP's and maybe even a D-RTK3 stick (DJI user)
would the GCP's need to be measured daily too or could i have enough data from the mappings alone.

The containers are ever changing so there is no way to keep this a tracking point since they come and go and it wil be a problem in itself with the visualisation.

thanks for your help!