r/UAVmapping

Consumer drone and terrain following produced 0.92 correlation with ground-truth timber volumes across 30 forest plots in British Columbia
▲ 20 r/UAVmapping+2 crossposts

Consumer drone and terrain following produced 0.92 correlation with ground-truth timber volumes across 30 forest plots in British Columbia

Wanted to share a study that just came out in The Forestry Chronicle where UgCS (drone flight planning software) was part of the methodology.

Researchers from BC Ministry of Forests used a Mavic 2 Pro with UgCS terrain following to fly 30 one-hectare plots across the Lakes Timber Supply Area. The aim was to estimate current timber volumes in managed lodgepole pine stands, 24 years after initial establishment, and compare them to growth model predictions.

The terrain following was the critical piece. These are rolling interior BC sites, and they needed consistent 1.0-1.5 cm GSD across every plot for the canopy height models to work. UgCS adjusted flight altitude continuously using DEM data so the camera stayed at a constant height above ground. About 200 images per hectare, processed in Agisoft Metashape.

Results: 0.92 correlation between the UAV-derived volume estimates and hand-measured ground verification plots. That's from a $1,500 consumer drone with proper flight planning, not a $50k LiDAR rig.

The forestry findings were sobering too. Only 56% of lodgepole pine were still healthy (down from 74% in 1997), and BC's standard growth models only matched reality when they plugged in updated site productivity and disease mortality numbers. The original model inputs were way off.

Full case study with specs and methodology: https://www.sphengineering.com/news/ugcs-terrain-following-uav-forest-inventory-british-columbia 

Citation: Woods, A., McCulloch, L., Watts, M. and Di Lucca, M. (2026). Bridging the gap between forecast growth and realized loss in managed forests. The Forestry Chronicle, 102(1): 44-58.

u/RobUgCS — 1 day ago

Thermal inspections/3dmapping/photogrammetry

Hi everybody
Hope you are all doing well

As someone who is still in the research phase, I would be glad to hear real experiences and advice from you guys about the business in general, risks, and a realistic timeframe to learn and implement these skills for someone who has never had experience with drone inspections but has been in the construction field for close to 10 years in Canada. I understand that different circumstances and facts are unique to every individual yet I believe there is some certainty as well which I’m looking to hear from you. I’m planning to purchase a Matrice 300 RTK with Zenmuse P1 and h20t once I’m done with my licenses and courses.
The plan is to learn gradually and not all at once. Going all in both intellectually and financially into this, what are my chances to succeed in this business?
Sorry if the question sounds too primitive or maybe even dumb, just trying to hear real opinions from real people with experience and not GPT or Claude which is very helpful but limited.

Thanks in advance!

reddit.com
u/SEdrone — 1 day ago

Rock Robotic Issues

We have used Rock Robotic since they released their R2A sensor. Using the R3 Pro V2 now, and having an absolutely horrible customer service experience. Feel like pulling my hair out, they're just talking in circles.

We currently aren't able to process anything; since the switch to the new dongle for the license this past month, processing has been very hit-or-miss if it will work or not. This is true even when pulling datasets from a year ago and trying to re-process them from scratch. Sometimes running the same data set through twice will get two different results. Are we missing something or is this a common experience with Rock? Dealing with their customer service is truly frustrating.

reddit.com
u/icanmakesound — 2 days ago

What enterprise clients actually require from drone contractors — a breakdown of what changes when you move up from small commercial work

If you've been doing commercial drone work for a year or two and you're starting to look at the bigger contracts — utilities, telecom, insurance carriers, large general contractors — the requirements feel different in ways the marketing materials don't quite explain.

I've spent the last few weeks researching what's actually different and talking to operators currently doing this work. Posting what I've found in case it's useful to anyone else thinking about this move. Not selling anything, no product, just trying to map the landscape.

The pattern, simplified: small clients buy the photos. Enterprise clients buy the documentation around the photos. The technical flying may not be much harder. The administrative layer on top of it is what changes.

Five things show up in nearly every enterprise contractor specification:

1. Higher and structured insurance. $1M is fine for small commercial work. Utility and large construction contracts commonly require $2M-$5M; some specify $10M. But the dollar amount isn't the main thing that changes. Enterprise certificates require the client listed as additional insured, waiver of subrogation, specific endorsements for night/over-people/BVLOS operations, and a real aviation liability policy (not a drone rider on a general business policy — standard CGL almost always excludes aviation). App-based on-demand drone insurance usually doesn't meet these contract terms; operators discover this when their certificate gets rejected at COI review.

2. Verified personnel. Part 107 is the baseline. Enterprise clients verify recurrent training currency (the 24-calendar-month requirement), check for relevant waivers when applicable, sometimes require industry training (OSHA 10/30 for construction, NESC awareness for utility work, confined space for industrial), and for critical infrastructure work require background checks. NERC CIP-004 personnel training and access requirements come into play when you're doing work that touches Bulk Electric System assets.

3. Documented equipment. Part 47/48 registration, Remote ID serial recorded, maintenance records. For some federal and critical infrastructure work, the make and model of the aircraft is restricted — Blue UAS list or similar.

4. Specified deliverables. This is where the variation between clients gets visible. File formats (GeoTIFF, LAS/LAZ), coordinate systems (state plane with specific zone, NAD83 vs NAD2011, NAVD88), naming conventions tied to client asset IDs, metadata standards, accuracy/GSD requirements, specific submission methods. The same flight captured the same way can be accepted by one client and rejected by another because the deliverable structure differs.

5. Audit-readiness. Every job leaves a record that, six months later, can show exactly what happened — who flew, with what aircraft, under what authorization, following what checklist, with what evidence of currency. For small commercial work the deliverable IS the artifact. For enterprise work, the deliverable is one item in a documentation packet.

The pattern across industries: utilities, telecom, insurance carriers, large GCs — the specifics differ but the structure of the requirements is similar. Five things appearing repeatedly, with each client having slightly different specifics. The cumulative weight of managing all five for every job, for every client, is what makes this hard. It's not any single requirement.

What I'm learning from operators currently doing this work: the ones who handle it best have evolved their workflow to make these requirements close to invisible during day-to-day operations — encoded into templates and processes that run automatically. The ones who struggle have the technical capability to fly the missions but lose contracts at the procurement or audit stage because the paperwork doesn't quite hold together.

Genuinely curious whether this matches what others have seen. If you're doing enterprise drone work and any of this is wrong or incomplete, please correct me — I'm trying to get this right.

Particularly interested in:

  • What enterprise client surprised you the most with their requirements?
  • What's the requirement that took the longest to figure out how to meet?
  • Which client management/documentation tools (if any) actually help vs. just adding overhead?

Happy to share what I've heard from other operators in DMs if it's useful.

reddit.com
u/avshah2021 — 2 days ago

Radio Link Warning today

So for the first time I got a "radio link warning" on my Phantom 4 Pro (I know I know we need a new drone. Separate talk lol).

I was around 3k feet away, with a VO. I was also over a Fire Station.

Does anyone think it was the radio towers on the fire station? or Distance? Or maybe something different?

I flew it again just to test it out and noticed I didn't get the same warning when I turned around about 200' short of the firehouse.

reddit.com
u/Junior_Plankton_635 — 2 days ago
▲ 31 r/UAVmapping+3 crossposts

Geospatial Conferences 2027

Hello all!
My organization has tasked me with finding and suggesting up to 5 geospatial conferences for 2027 (will probably actually send people to 2 maybe 3).
I'm looking for quality professional conferences on the topics of GIS, Geospatial, Remote senseing, GeoAI and drone mapping. The conference can be anywhere world wide. (We already know about ESRI).
Anyone have any suggestions? Again this is for next year 2027.

thanks

reddit.com
u/eagerly_anticipating — 4 days ago

Zenmuse L3 and DJI Terra Question.

Hello I'm planning to purchase Zenmuse L3, I've been informed that you can only process data from zenmuse L3 using DJI terra, now i've been making research and found this link. It says that DJI Terra is free for Zenmuse L3.

my question is: is there difference between the "free" DJI Terra for Zenmuse L3 and the DJI Terra that I can buy?

u/Old_Draw4626 — 3 days ago

Flight Mission Comparison: Matrice 400 vs. Matrice 350 RTK (Large-Scale Projects)

When evaluating mission profiles for large-scale geospatial and wide-area mapping projects utilizing the Zenmuse P1 sensor, the strategic transition from the Matrice 350 RTK to the Matrice 400 highlights a monumental leap in operational throughput. Under the standard Matrice 350 configuration, restricted to a cruise speed of 15 m/s with an 80/60 overlap, flight durations for these massive blocks span between 29 and 31 minutes. While these times technically fit within the M350’s endurance, they push the batteries to their absolute threshold in real-world environments, leaving virtually no margin for unexpected wind resistance or complex terrain fluctuations.

Deploying the Zenmuse P1 on the Matrice 400 at an optimized speed of 20 m/s completely redefines efficiency in wide-area data acquisition. Utilizing the M400’s enhanced propulsion and superior wind management, the actual mission times for identical extensive coverages drop down to a highly efficient 21 to 24 minutes—accounting for real-world banking turns and terrain-following deceleration. Even with the P1 triggering at a rapid cadence (every 1.4 to 1.9 seconds) to maintain strict data density at high speeds, its 0.7-second mechanical shutter easily sustains the pace without data gaps. Ultimately, running the Matrice 400 at 20 m/s allows these extensive blocks to be cleared comfortably on a single battery set with a secure power reserve, dramatically increasing daily hectare production and optimizing field logistics for high-volume workflows. Furthermore, I will validate these calculated data sets directly during the upcoming field operations and keep you informed of the real-world performance outcomes.

u/Aggressive_Call4165 — 5 days ago

AI or algorithmic ways to modify vegetation in a 3D model while keeping it photoreal?

Hey everyone,

I'm flying a DJI Matrice 4D and processing the data in DJI Terra (orthophoto, DSM, textured mesh / B3DM). For some projects I'd like to change the vegetation in the resulting 3D model while keeping the rest of the scene as photorealistic as possible.

Example: a site is captured in summer with full foliage, but I want to show the same area in winter (snow) colors, with denser forest, different tree species, or partially cleared. Basically swap or modify the green stuff without the result looking like an obvious CGI paste-in.

Another problem is, that the trees are not looking good when zooming in (video), for the projects I'm doing, it would rather keep the flight height at about 120m AGL, otherwise it takes ages in flight-time.

A few things I'm wondering about:

  • Any AI-based workflows that can isolate vegetation in a textured mesh (segmentation + inpainting, diffusion-based texture replacement, etc.) and replace it convincingly?
  • Algorithmic approaches that detect vegetation first (NDVI on the ortho, height filtering via DSM, mesh classification) and then substitute it with library assets like SpeedTree or Quixel that actually blend with the surrounding terrain?
  • Has anyone found a pipeline that handles the transition between modified vegetation and untouched ground without visible seams?

I've been experimenting in Blender but the moment I drop in standard 3D tree assets it immediately looks fake next to the photogrammetry texture. Curious if anyone has cracked this, either with commercial tools, research projects, or some clever Blender/Unreal hack.

Cheers

u/mountain_mapping — 3 days ago
▲ 531 r/UAVmapping+2 crossposts

Turned a whole neighborhood into 3D (Mini 5)

A friend of mine took his Mini 5 out for a neighborhood flyover last week and converted it into a 3D scene → https://teleport.varjo.com/captures/524ee89f293a4a2e907009191ba7b9f4?viewer=v3

Scene is ready a few hours later. Wasn't sure how it'd hold up, but the result came out better than expected.

Heard DJI Terra can do similar stuff but it's super expensive. This was way easier & more accessible. Has anyone else here tried drone-to-3D?

u/sebgr1 — 6 days ago
▲ 71 r/UAVmapping+3 crossposts

I created a (partial) 3D scene of Pinawa Dam from one of my Winter visits

u/Armand9x — 5 days ago

What is the bare minimum?

Hello everyone,

I am starting in this world and i would be interested in mapping/fotogrametry/3D scanning. What would be the bare minimum for an acceptable result? Neo 2? Mini 4 pro? Lito X1?

Of course, i am thinking not the most efficient (manual flight, small area, many pictures to show enought detail, slow speed ans therefore longer times...i dont care that much about all that stuff as long as the results are presentable.

Thoughts?

Cheers!

reddit.com
u/No-Spirit8373 — 5 days ago

New Drone For Our Fleet

We have recently bought new Matrice 400 for our Cadastre and Mapping missions in Turkiye. 3 Batteries, Bs100 and Zenmuse P1 is included. I will share my experiences about it and compare with our Matrice 350 RTK. I would really appreciate it if you could share your experiences and what I should pay attention when fliying with Matrice 400.

u/Aggressive_Call4165 — 6 days ago

Low altitude mapping

Hi. I have a DJI mavic 3M drone. I saw some research papers mentioned that they collect images at 5 meter (15/16 ft) altitude. But I can see the least option is 39.4 ft. Is there any way to fly drone at 15 ft altitude using area route by DJI mavic 3M?

reddit.com
u/md_porom — 7 days ago
▲ 4 r/UAVmapping+2 crossposts

[Open source] Need a 10–30s raw .SRT from your Mini 5 Pro / Air 3S / Mavic 4 Pro / Neo / Flip / Avata 360 — help us add parser support

Hi all — I help maintain dji-drone-metadata-embedder, an open-source Python tool that bakes DJI telemetry from .SRT files into the matching MP4 (subtitle track + GPS metadata) and exports flight tracks to GPX / CSV / JSON. Mini 3/4 Pro, Air 3, Avata 2, and Mavic 3 Enterprise are already first-class.

We've surveyed the post-2024 lineup and none of the new models has a publicly available SRT we can write a parser against. So we're asking the community: if you own one of these and can spare a couple of minutes, you'd unlock first-class support for the model in our next release.

Models we'd love samples from (any of them helps):

  • DJI Mini 5 Pro
  • DJI Air 3S
  • DJI Mavic 4 Pro
  • DJI Neo
  • DJI Flip
  • DJI Avata 360

What we need:

  1. Enable "Video Subtitles" in DJI Fly before you record (Settings → Camera → Video Subtitles). Without this no .SRT is generated.
  2. Fly a brief hover — 10–30 seconds is plenty. No sensitive location needed; we don't need beautiful footage, just a clean SRT with all telemetry tokens populated.
  3. Upload the raw .SRT to Google Drive / Dropbox / GitHub Gist / Pastebin (raw) and share the link in a comment or DM. ⚠️ Do NOT paste the SRT contents into a Reddit comment — Reddit's markdown silently strips the delimiter spacing and breaks the format. The file has to arrive byte-exact.

Bonus asks (only if convenient):

  • Air 3S: one clip in D-Log M and one in Rec.709 — helps us map the color-profile token.
  • Mavic 4 Pro: change focal length (zoom) mid-clip — exposes the dynamic focal-length token.
  • Anyone: if your drone also writes a .LRF or .DAT next to the MP4, those help end-to-end validation but are not required.

Privacy: GPS coordinates can be anonymised before the file is committed as a test fixture if you prefer — just say so. Your handle will be credited in the changelog unless you'd rather stay anonymous.

Thanks! Even one good sample meaningfully moves a model from "best-effort" to "fully tested with golden fixtures."

u/Caygill — 5 days ago
▲ 347 r/UAVmapping+4 crossposts

1 km² 3D Gaussian Splat of Mjøssykehuset | Drone + LichtFeld Studio

This is not video footage. It is a camera animation inside a 3D Gaussian Splat trained from drone photos.

The dataset was captured with a DJI Matrice 4 Enterprise over roughly 1 km² around the planned Mjøssykehuset hospital project in Moelv, Norway. The goal is to explore how lifelike 3D reality capture can help visualize construction and infrastructure projects in their real-world surroundings.

This version shows the captured environment only. The next step will be to combine the scene with BIM / design models, allowing project teams and stakeholders to better understand how a future building fits into the actual site.

Technical details:
5517 photos / 36.5 GB dataset
Trained with LichtFeld Studio
20M splats, SH2, PPISP, MrNeRF strategy
550k iterations
~23 hours total training time
RTX 5090 + RTX 6000 Ada

Capture + processing:
DJI Matrice 4 Enterprise
RealityScan + LichtFeld Studio

4k version: https://www.youtube.com/watch?v=tvruJz8Lj0w

Drone capture and 3D Gaussian Splat by Alos Engineering

u/inkedflight — 10 days ago
▲ 1 r/UAVmapping+1 crossposts

how to build an AI algorithm in UAV

I am a master student major in AI in UAV.I now have a project about equipping the drone with a parachute to reduce its crash damage.Now I am in charge of the part to build an AI algorithm to calculate the best time to open parachute.But I totally don't know from where to begin!!!!It almost drives me crazy!!!!!

If you can help me on anything about this project,please tell me.

Some questions like below:

Is PX4 suitable for this project?

How can I use PX4 do to control the parachute?

What open source project can I refer to?

Or anything you want to tell me.Thanks!

reddit.com
u/Confident-Ear-1090 — 6 days ago
▲ 385 r/UAVmapping+5 crossposts

I used one of my Micro Drone videos to process and recreate the scene as a 3D Gaussian Splat

u/Armand9x — 12 days ago

Phantom 3 Professional in 2026?

Hello! I’ve been interested in drone mapping for a while, but I never had the opportunity to get a proper drone. Last month, I picked up a P3P at a garage sale. It came with three replacement batteries, two sets of OEM props, one set of carbon props, and a carrying bag.

I’ve already had it inspected and tested, and the only issue found was that the batteries are worn and should be replaced (max possible flight time is about 5 minutes).

I’m interested in hearing people’s opinions and experiences on whether a P3P can still be used to start a mapping business nowadays, mainly for stockpile measurements or agriculture.

As I understand it, the current go-to solution for precise measurements is an RTK setup, but I’ve also seen people say that using GCPs alone can be accurate enough for mapping. Would a P3P with a proper GCP workflow still be viable, or is it too outdated for professional use today?

reddit.com
u/netesfiu — 7 days ago

UAV-Based Road Design: Ensuring Vertical (Z) Precision in Long-Distance Corridors

Hello everyone,

I am currently working on a 50 km (31 miles) long road design project. We are using a DJI Matrice 4E along with a D-RTK 3 mobile station. The data we obtain will serve as the primary base for engineering design (road project), so vertical (Z) accuracy is our top priority.

To establish the control network, we performed 2-hour static observations on our base points and obtained the coordinates this way. However, I have some concerns about maintaining relative accuracy over such a long distance:

  1. Potential Issues: What are the most common accuracy errors encountered in these types of long corridor flights?
  2. Network Adjustment: Even though I have 2-hour static data for each point, should I perform a global least-squares network adjustment to tie all these points together? Or is it sufficient to calculate points individually and proceed with the RTK flight?
  3. Consistency: Since road design (drainage and slope calculations) is involved, how can I guarantee a balanced model without "steps" or jumps between different flight blocks?

I look forward to hearing your insights and experiences.

reddit.com
u/Witty-Accountant-250 — 10 days ago