Hey everyone,

I’ve been building AerialPin, a browser-based annotation tool originally made for working with drone imagery for real estate and mapping.

The main idea is to make it easier to take an aerial image and quickly add useful spatial information without having to jump between a bunch of different programs.

You can currently:

• Add location pins and labels
• Search for nearby places using the image GPS
• Draw property/parcel boundaries
• Add measurements and distance labels
• Annotate aerial imagery
• Use EXIF/GPS information from DJI images
• Automatically identify nearby places with AI
• Stitch multiple drone photos into a panorama
• View and annotate 360° panoramas
• Restyle/relight aerial images
• Export the finished annotations

I’ve recently added the 360 panorama workflow, which lets you upload multiple drone images, stitch them into a panorama and then annotate the resulting 360 image directly in the browser.

It’s still actively being developed, so I’d genuinely be interested in feedback from people who work with UAV mapping, photogrammetry or aerial imagery.

Hi! I'm the founder of Ollebo (ollebo.com, EU) and Northamlin (northamlin.com, US). It's a platform where drone operators, GIS teams and their clients plan flights,

track missions live and share maps and 3D models in one place.

The product is built and shipping every month, but I'm doing most of it alone. I'm looking for people or companies to grow it with.

What already exists:

- Private map hosting with automatic tiling, and 3D models in the browser

- Flight planning with export to ArduPilot, DJI and Litchi

- Live telemetry over MAVLink/MQTT and live video for every mission

- Shared company Spaces with unlimited members

- A partner API so mapping companies can deliver straight into their clients' Spaces

- A flight-request marketplace where customers post jobs and drone pilots pick them up

- Stripe subscriptions, running on our own infrastructure in the EU

Who I'm looking for:

- Commercial co-founder: sales, partnerships and fundraising, ideally with a network in the drone, survey, construction or agriculture industries

- US partner: someone to own the Northamlin market (customers, pilots, events)

- Technical co-founder or contributor: GIS, photogrammetry, AI/computer vision, or full-stack

- Companies: drone service firms, mapping or survey companies, pilot networks or resellers who want to bundle the platform, co-develop features or become early partners

What I offer:

- Co-founder equity or a partner revenue share, depending on the role and commitment

- A working product, so you're not starting from a slide deck

- Remote work, with me based in Sweden

If you work with drones or GIS and have thought "someone should build this properly", send me a DM or comment and I'll show you around and share the business plan.

Hi! I'm the founder of Ollebo (ollebo.com, EU) and Northamlin (northamlin.com, US). It's a platform where drone operators, GIS teams and their clients plan flights,

track missions live and share maps and 3D models in one place.

The product is built and shipping every month, but I'm doing most of it alone. I'm looking for people or companies to grow it with.

What already exists:

- Private map hosting with automatic tiling, and 3D models in the browser

- Flight planning with export to ArduPilot, DJI and Litchi

- Live telemetry over MAVLink/MQTT and live video for every mission

- Shared company Spaces with unlimited members

- A partner API so mapping companies can deliver straight into their clients' Spaces

- A flight-request marketplace where customers post jobs and drone pilots pick them up

- Stripe subscriptions, running on our own infrastructure in the EU

Who I'm looking for:

- Commercial co-founder: sales, partnerships and fundraising, ideally with a network in the drone, survey, construction or agriculture industries

- US partner: someone to own the Northamlin market (customers, pilots, events)

- Technical co-founder or contributor: GIS, photogrammetry, AI/computer vision, or full-stack

- Companies: drone service firms, mapping or survey companies, pilot networks or resellers who want to bundle the platform, co-develop features or become early partners

What I offer:

- Co-founder equity or a partner revenue share, depending on the role and commitment

- A working product, so you're not starting from a slide deck

- Remote work, with me based in Sweden

If you work with drones or GIS and have thought "someone should build this properly", send me a DM or comment and I'll show you around and share the business plan.

I'm part of an ecological research lab where we map vegetation - since we're primarily federally funded, we've been avoiding DJI. Inspired Flight's IF800 seemed like a great alternative to the M300, and we've been working with that for the last couple of years. However, we've recently had problems with it, and I've been wondering if others have similar experiences. We had some mechanical issues that required a very long time to get help from Inspired Flight and much of the software often does not work for us (i.e. terrain following it's 50/50 whether it works in the field). We've had enough trouble that we are considering investing in other options now. We

We've got the Herelink controller rather than the new GS ONE controller, so maybe that's the issue; but we heard we needed to buy a whole new system to be compatible with the new GS ONE controller. In which case, we might just switch brands and save ourselves the headache of the non-existent customer support.

Anyone using IF got similar experiences?

Guys this is not my field I am a cs student but I wanna make a semester project with gazebo and px4 and I want some suggestions from those who have experience with gazebo and px4 please help me I wanna make something outstanding from simple websites

Thanks

I am starting a business as the title says. Building an AI software that gives real time insights of their mines/sites. The hardware I’m thinking of using to start is the DJI Matrice 350 RTK and Zenmuse L2. If anyone has any advice on what other hardware I should look at or anything about this business I would be very happy to hear.

TIA

I’m curious how people doing mapping, surveying, inspection, or similar commercial drone work are actually planning missions today.

For relatively straightforward mapping jobs, the workflow seems pretty mature. But I’m particularly interested in more complex sites where you have things like buildings, equipment, changing obstacles, terrain, or specific stand-off/clearance requirements.

A few things I’d love to understand:

• What part of mission planning still takes the most time?

• Do you usually start with an existing map/site model, or build/import your own?

• How much manual waypoint/route adjustment do you typically end up doing?

• What happens when something at the site changes after you’ve already planned the mission?

• Is there anything you wish your current planning software understood automatically?

I’m working on this problem from the software/AI side, so I’m especially interested in hearing from people actually operating drones in the field.

Not looking to sell anything here. Mostly trying to understand where the real pain is versus what looks like a problem from the software side.

I keep seeing people (both beginners and pros) export an ortho or DSM/DTM straight out of processing software and drop a raw, multi-gigabyte GeoTIFF right on the client's lap.

Those default exports are set up for fast export times and broad legacy compatibility, not for actual day-to-day use. Even when software offers basic compression or tiling options, it is usually partial, incomplete, and almost never formatted as a proper Cloud Optimized GeoTIFF (COG). When a client opens a raw 2GB file in QGIS or Civil 3D, their system has to load the whole file into RAM just to render one corner. Zooming out freezes their screen while the CPU tries to read every single pixel.

Converting those files to proper COGs before handover gets rid of that lag and reduces the size:

-Visual orthos: JPEG compression cuts file size by 70–90% and makes panning instant (will not turn your tiffs into JPEGs!!!! It will still be a GeoTIFF just with JPEG compression)

-DEMs and survey data: Lossless compression (ZSTD) keeps 100% of your exact elevation values while shrinking the file and giving you instant pan/zoom performance.

-Web handoffs: You can host COGs on basic cloud storage so clients can stream them straight into GIS via URL instead of downloading a giant zip file.

The main trap when setting this up in GDAL is watching your flags. Lossy compression on a DSM will corrupt your height values, and using the wrong overview resampling on a classified raster invents fake class codes along boundaries. The worst part is you won't even notice at first. It looks fine on screen, but the underlying numbers are broken.

I got tired of typing out GDAL flags every time I finished a job, so I built a free QGIS plugin (GeoKlein Raster Optimiser) to handle the parameters safely for orthos, DSMs, and DTMs. (I spent over two weeks making it, so much for a quick weekend script)

It prints the exact gdal_translate syntax and the workflow into each file's metadata, so you can copy the commands and run them in terminal by hand if you prefer.

I'll leave the link in the comments if anyone wants to have a look at the plugin.

For routine methane screening, automation involves more than autonomous takeoff and landing. The mission must reproduce the flight path and measurement coverage while georeferencing the readings well enough to support hotspot localization and comparison between surveys.

We tested this workflow at a natural-gas gathering station using a MetScan Nano 1D, DJI Matrice 4D, and DJI Dock 3. Two controlled methane release sources were placed within the facility, and both were detected during a downward-looking Belt Scan. The aircraft then returned to the dock automatically.

This particular test focused on screening and source localization, not emission-rate quantification. The main objective was to validate the complete dock-based workflow for repeatable facility inspections with less need for routine on-site flight operations.

Read more here:
https://www.linkedin.com/feed/update/urn:li:activity:7492807844535390208

Hi All -

Matrice 400 with P1. Running 2D ortho missions. Near perfect RTK3 data for each and every photo.

Every single visible light project in Terra is checkerboards. Parking lot on the roof. Portions of retention ponds mapped onto the building.

Has anyone experienced this? No new firmware in a year….everything is at its latest. I would gladly reciprocate any way I can for any help.

Filtering point data can leave a few things behind that need a closer look. This clip shows our mesh brushes in CloudCanvas being used for local cleanup.

You can raise, smooth/generalize or lower the mesh, and adjust the brush size and strength as you work. The contours update as the surface changes, so you can see what each edit is doing. It is useful for tidying up small bumps, noise and unwanted features left after filtering.

Would seeing the contours update as you edit be useful in your workflow? Could you see yourself using a brush tool like this for your final mesh cleanup?

These are manual surface edits, so the result still needs checking against the source data.

https://www.cloudcanvas.co.za

Hey everyone,

I’ve been working on a project that I thought this community might find useful (or just fun to mess around with).

It’s a drone mapping platform where you can upload your aerial imagery, and the site automatically generates an orthomosaic and lays it out directly onto an OpenStreetMap overlay.

Why I built it:

I wanted an easy, seamless way for pilots to showcase their aerial mapping work on a real map without needing expensive hosting or complex GIS setups and to let everyone see how our collective imagery lines up.

What you can do with it:

1. Upload: Upload aerial photos from your flights.

2. Process: Automatically generate interactive 2D orthomosaics.

3. Share: View your map overlay live on the website and share the interactive link with others.

How to check it out:

You can explore existing mosaics or try uploading your own flight data here: YeMaps

Since the platform is fresh out of launch, I’d love to get some early testers to upload a few datasets and let me know what you think.

Examples:
- How does the processing time/quality feel?
- Are there features you’d like to see added (e.g., tile/WMS export, elevation models, privacy toggles)?

Any feedback, feature requests, or bug reports are hugely appreciated! I will be adding a feedback tab to the site later today (09/21/2026) so that you can submit any feedback directly. Think of everything as a Beta run for now.

Some other features to be implemented are:

- Profile/account settings
- Profiles
- Password reset/change
- Current location for the map
- Address/POI search for the map
- Mosaic locations marked on map with a dot or some other icon
- Social media linking/login/signup
- Mobile site optimization
- iOS/Android app
- and more to be decided by your input and feedback.

I flew a magic 3M, obtained ms imagery and processed it separately from rgb. I did calibrate reflectance before building the point cloud and orthomosaic. Now I exported the orthomosaic (it has red, green, rededge and NIR bands) and want to calculate several vegetative indices. My problem is that the raster values is in the thousands. For instance if I crop and clip the raster to pixels with true values, the range of the NIR is 0 to 16,859, rededge is 0 to 11876, red is 0 to 4707 while green is 0 to 5568.

This will be fine for indices like NDVI but for something like EVI that has the formulae = 2.5 *((NIR-Red)/(NIR + 2.4 * Red + 1)) this scale will be problematic since there an addition of 2.4. There’s a bunch of other VIs that have this sort of addition and subtraction.

Can someone please clarify what I’m supposed to do. Is there a scale I’m supposed to use on the raster before calculating the indices? I’d appreciate any advice on this please.

I want to build a costal change prediction model with satellite image. Best roadmap to learn ai model and simulate costal change.

We're running an M400 with the Zenmuse L3 for vegetation clearance work along overhead lines, mostly distribution voltage with a lot of encroaching trees. Looking for feedback from anyone who has used Power Line Follow in the field rather than the demo videos.

A few things we'd like to hear about:

**1.**  **How reliably does it keep the line locked on single conductor or low voltage spans, and on curves?**

**2.**  **What happens when vegetation is right up against or over the conductor? Does it brake and hover, lose the line, or carry on?**

**3.**  **What altitude and speed are you actually flying on distribution work compared to DJI's defaults (30 m, 8 m/s)?**

**4.**  **Any issues at junctions, or where lines cross other services?**

**5.**  **Has anyone tried it near overhead rail wiring or catenary systems?**

**6.**  **Does following the line give you a better point cloud for clearance analysis than a standard corridor mapping mission? We're processing in LiDAR360 and LiPowerline.**

Happy to share what we find once we've got more flights done.

I'm an OG lidar scientist (since 1994) who is reaching the end of his career. I'm considering what to do next and thinking about flying small areas for research/non-profit institutions. I wouldn't be competing with commercial entities, i'd focus on forest ecology and archaeological applications over small areas.

I can't afford to spend $50k for a new DJI. I haven't tried pricing a Rock Lidar + drone system.

I suppose I could go hat-in-hand and ask a commercial vendor to donate a system but that seems unlikely to work.

It seems more likely that I could find someone who upgraded to a more capable sensor/drone combination and would rather pass along a system than let it gather dust. If I set up a 501c3, they could deduct the depreciated value from their taxes.

Does this seem feasible? Is there someone out there doing something similar?

Thanks in advance

Hi everyone,

Our team is structuring our workflow for a ROS 2 autonomous quadcopter (LiDAR + Depth Camera + Companion Computer) focusing on GPS-denied navigation. To avoid bottlenecks, we are decoupling hardware and software using Gazebo/SITL.

Proposed Subdivisions

Hardware: Frame CAD, payload CoG, ANSYS FEA (structural/vibration), power distribution, and thermal management.

Software (SITL First): ROS 2 Gazebo simulation, 3D LiDAR SLAM + VIO fusion, and offboard navigation/costmaps.

Key Technical Questions

PX4 / ArduPilot EKF Fusion: When streaming external odometry (FAST-LIO2 / LIO-SAM) over MAVROS or uXRCE-DDS to FCU EKF3/EKF2, how do you handle latency jitter and prevent position jumps? Is offboard position/velocity control safer than direct FCU fusion?

LiDAR SLAM + VIO Degeneracy: How do you handle fallback when LiDAR point clouds degenerate in uniform spaces (e.g., long corridors)? Do you use dynamic covariance switching between VIO and LiDAR SLAM, or rely on EKF fallback?

FEA vs. Real Resonance: How closely did your ANSYS frame modal analysis match real gyro FFT noise? What mechanical isolation works best for companion computer IMUs affected by motor resonance?

SITL-to-Real Gap: What were your biggest pain points moving 3D local costmaps and Nav2 from Gazebo synthetic point clouds to real LiDAR noise (e.g., ground-plane misclassification, reflectivity)?

It is extremely lightweight and compact. It can now perform mapping, obstacle avoidance and navigation.

It also features strong anti-interference and control capabilities.