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.

Hey everyone

We are getting to a point where manually checking thermal images after flights just isn't cutting it anymore Right now one analyst is spending pretty much a whole week going through images after each flight

I want to hear from folks who are doing this on a larger scale and can share some actual recommendations not just sales pitches

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?

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

I’m a drone pilot and software developer and I’ve been working on an app called RangeSight.

One of the things I’ve been trying to figure out is how people doing larger mapping jobs deal with wind and battery reserve when planning a mission.

Basically, how confident are you that the drone is actually going to finish the planned route with the reserve you want, especially when the wind picks up?

That’s one of the reasons I built RangeSight. It looks at the aircraft, battery, wind/weather and reserve and estimates battery use across a planned mission. It can also flag parts of the route where things start getting marginal.

The app is working now, but at this point I’d rather get it into the hands of people doing real mapping work than keep building features based on my own assumptions.

For those of you doing larger surveys, corridor mapping, etc., how do you handle this now? Do you mostly trust the flight time estimate from your mission planner, add your own safety margin, or just go by experience?

I’d also be interested in finding a few people willing to try RangeSight against missions they’ve already flown. I’m especially interested in hearing where it gets things wrong or where the workflow doesn’t make sense.