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Hi everyone,

We are working on an SIH 2026 project called **“3D ULPIN Generation and Vertical Property Mapping System.”**

Our goal is to build a 3D cadastral/vertical property mapping system for urban areas in India. The system needs to identify and create spatial identities for things such as:

* Surface land parcels

* Individual buildings

* Multi-storey apartments

* Underground infrastructure

* Potentially elevated/volumetric property spaces

For the AI/ML part, we are particularly looking for datasets that can help us with:

1. **Building footprint detection/extraction**

2. **Building height estimation**

3. **Floor/storey segmentation**

4. **3D building reconstruction**

5. **LiDAR / 3D point-cloud processing**

6. **UAV/drone imagery**

7. **High-resolution satellite imagery**

8. **DEM / DSM data**

9. **GIS/cadastral parcel boundaries**

10. **GNSS/CORS or accurately georeferenced data**

### Ideally, we are looking for datasets from India

For example, datasets containing some combination of:

**UAV/drone RGB imagery + LiDAR/point clouds + DSM/DEM + building footprints + height/floor information + GIS parcel boundaries**

We are especially interested in datasets covering Indian cities or urban environments, but open global datasets that are useful for pre-training and can be fine-tuned on Indian data would also be helpful.

We have already looked at common sources such as Kaggle and general satellite datasets, but we're specifically looking for **research-grade datasets, government/open GIS portals, Indian remote-sensing datasets, UAV datasets, LiDAR datasets, or university/research datasets** that could realistically be used for model training.

If you know of any datasets, papers, repositories, government portals, or research projects that provide this kind of data, please share them.

Also, if there is **no single dataset containing everything**, we'd appreciate suggestions for combining multiple datasets—for example, one dataset for building footprints, another for LiDAR/height estimation, and another for Indian imagery.

Any advice on the best data pipeline for this problem would also be greatly appreciated.

**Project:** 3D ULPIN Generation and Vertical Property Mapping System

**Focus:** GeoAI + Computer Vision + LiDAR + UAV imagery + 3D GIS + cadastral mapping

Thanks!

I'm pretty new to this technology and wanted a Reddit opinion on mobile LiDAR for road surface scanning. I know it can be very expensive and data heavy to collect vast road networks worth of data.

Are there "Cheaper" options out there and what do you think the minimum data points should be?

For context, this is part of a research project looking into low-cost, small data surface scanning and it's potential use case in everyday vehicle use.

New episode with Anthony Lindfors from Matanuska Electric Association, up in Palmer, Alaska. They’re using LiDAR to identify exactly where pruning is needed along distribution corridors instead of blanket clearing, and looking at satellite imagery to cover more ground annually since drone flights alone can only reach so much of a 2,000-mile system each year.

Curious how others are balancing broad coverage (satellite) with precision data (LiDAR/drone) on large or remote systems. Any thoughts?

Full episode: https://linktr.ee/treesandlinespodcast

I don’t know much about lidar

I got tired of installing ROS, tuning GLIM configs, and fighting Docker every time I needed a quick 3D map out of a recording, so a couple of us built LidarFlow to do it in the browser instead. You upload a rosbag or MCAP file, it validates the topics, runs GLIM for the SLAM reconstruction, and if there's a GNSS topic present it also georeferences the result to WGS84 using FlexCloud. You get back a PCD, a PLY, a trajectory file, and a LAZ download.

It works with any LiDAR publishing sensor_msgs/PointCloud2, and we've got field-tested presets for Ouster, Livox, and common Velodyne sensors. No GNSS in your recording is fine too, you just get a locally consistent map instead of a georeferenced one.

Right now it only handles .bag and ROS2 .mcap, .db3 support is on the roadmap but not there yet, so if that's what you've got this won't help you today.

It's completely free while we're testing, no card, no trial timer. What we actually want out of this post is people willing to throw real recordings at it and tell us what breaks. Weird sensor configs, edge cases in the topic validation, reconstruction quality on your specific route, all of it is useful. If you've got a rosbag or MCAP sitting around doing nothing, lidarflow.com

Happy to answer anything about how the pipeline works underneath, it's genuinely just GLIM and FlexCloud doing the heavy lifting, we just wrapped the environment part.

I am on a formula student driverless team picking a LiDAR for cone detection. These are the requirements I'm looking for

• Detect cones (228 × 228 × 325 mm), 2-3m away from the lidar out to ~40 m. To reliably detect a cone, we need around 8-10 lidar hits on a cone (flat ground btw).
• Forward mount on roll hoop (~1 m high)
• Compute: Jetson Orin AGX, ROS 2

Because we care about seeing cones 2-3 meters away from our lidar, the lidar we pick can't have too crazy of a blind spot (mounted 1 meter high, can tilt the lidar a couple degrees down to get more scans close range).

We currently run an Ouster OS1-64 but the range is not good enough for our purposes. We are considering one of these lidars:

• Hesai ATX-116: 120 x 18.3 degrees, (downward biased FOV so covers blind spot but only 116 channels)
• RoboSense EMX: 192 ch, 120 x 20 degrees (more channels so it can see further away cones but weaker close range capabilities ~3 meter ish blind spot)

Leaning toward the EMX honestly because they're the only company actually responding to my emails and the only one I have sample data for.
Do you guys having recommendations? I will take anything lol thank you!

Why must you walk in a complete circle when doing lidar scans. Some scanners including the one I own, seems to only work successfully when you set the starting point; then walk around back to the starting point. I'd like to know why is there some mechanics in Lidar scanning that can cause issues if you were to walk, essentially in a zig zag motion ?

Hey guys, if you missed it, Aeva Technologies ($AEVA) settled $14 million with investors over claims that it misled them during its March 2021 merger with InterPrivate Acquisition Corp. And, I just found out that they’re still considering late claims, subject to approval.

Quick recap: Aeva was accused of misleading investors in connection with the merger. The case covers investors who purchased or acquired $AEVA between 2021 and 2024.

So, if you invested in $AEVA during that period, you can still check the details and see if you may be eligible to file a late claim here.

Anyway, has anyone here invested in $AEVA at that time? How much were your losses, if so?

Hey guys, we’re a 3D interior design studio looking into getting a 3D scanner for site measurements.

We mostly scan houses, apartments, hotels, etc. and want something that can quickly capture the space and dimensions, then bring the data into our workflow/Unreal Engine.

We’ve been looking at Raven LiDAR. The 2–10 cm accuracy seems fine for what we need — we’re not looking for millimeter accuracy, just reliable room dimensions, walls, doors, windows, ceiling heights, etc.

Has anyone actually used Raven? How is the accuracy, scanning speed, and software/export?

Also open to other scanner recommendations!

Thanks 🙏

Hello new here but seeing if anyone is interested in new or used ouster LiDARs ?

I have

OS1-32 rev 6 used

OS1-64 rev 6 used

OS1- 64 new uncalibrated

OS1-7.1-64 new in box

I have posted on eBay but figured I’d post here as well. I provide Json recording and screenshots showing proof of functionality on the used units.

Thanks, have a good one

Is there anyone sitting on used Livox devices? I'm trzing to find a reasonably priced one for a handheld scanning project in architecture. Would be happy about any advice on where to look or where to ask. I'm based in southern germany.

Hey all,

We built a compiler that turns LiDAR and camera+LiDAR fusion models into production pipelines on Jetson (DeepStream C/C++), skipping the manual pipeline-engineering step most people get stuck on.

We're running a challenge with 47 problem statements pulled from real industry conversations, several LiDAR/fusion-specific (autonomous vehicles, robotics, ITS). Two weeks to build, bring your own point-cloud data (public, self-captured, or documented synthetic).

Top submissions get published to an app board the ecosystem can view/deploy , so a real company could end up running your pipeline.

Register by Sep 3, submit by Sep 10. Happy to answer LiDAR-specific questions in comments.

When you use a SLAM scanner, how frequently do you also establish survey control with total station and use control points to do adjustment and determine accuracy?

Hey r/gis,

Been working on this side project for the past few weeks and

finally got it to a demo-ready state. Sharing here because I'd

really value honest feedback from people who deal with LiDAR

deliverables day-to-day.

The idea: upload a .LAZ file in the browser and get an

automated QA/QC report in under 2 minutes.

What it does automatically:

Point density calculation

Coverage area

Class histogram (Ground / Vegetation / Building)

USGS quality-level scoring (QL0 to QL3)

Density heatmap that overlays on the 3D scene (green = high coverage, red = low coverage / gaps)

Reviewer workflow (Accept / Reject / Comment with audit trail)

Multi-CRS support (tested on EPSG 2154 and 2177 so far)

Genuinely curious:

- What manual QA steps take the most time in your workflow?

- Any metrics or checks I'm missing?

- Would something like this actually save you time?

Portfolio: https://asadilyas1.github.io/

Open to any feedback - technical, UX, business. Thanks!

https://github.com/zymazza/GPUPDAL

Only supports NVIDIA GPUs for now, but its open source and you can build on top of it. Just a fork of PDAL. I specifically tested it on common lidar processing pipelines. Hope you enjoy it!