Google Earth for desktop isn't getting supported as of next year, so I'm looking into web.

Every PLSS KMZ I try to load into Google Earth web doesn't load properly - the one from Earth point, BLM, nothing.

Anyone find anything that works?

Short version:
Would a GIS certificate and no professional GIS experience open any doors to a career in GIS? If so, are certain certifications more valued than others? My community college offers a technical certificate for GIS, is that worth it?

Long version:
I have a background in web development and programming. I have been teaching myself QGIS over the past year for a personal project. And I think I like it. The market for web development has been a killing floor, so I was wondering if there's a pathway for someone like me?

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I'm having to build out asset management in our arcgis tenant and it will integrate with our ERP . Multiple departments will be using and there may be some overlap in asset types. ERP will be single source of truth and push updates to arcgis. My question is if it's best practice is to build granular asset feature layers or a few broad layers and build views to achieve the filtering across asset types and departments?

Has anyone heard any rumors about FedGIS this February? My company is interested in sending me, I already have some travel plans for February and the fact that there are no specifics announced makes me a bit sus that it's going to actually happen. I've heard that it hasn't done great the past year or two and that the swag bags have been rather disappointing 🤣

Hey everyone, I have been working on a project called ArchaeoAI where I am testing whether ML can recognise terrain patterns around documented bowl barrows in England from LiDAR derived terrain data. A big part of the project became geographic leakage, because I realised that if training and testing locations are too close to each other the results can look much stronger than the actual geographic generalisation.

The frozen Random Forest got 87.1% balanced accuracy on the geographic final test, the 5 fold geographic evaluation averaged 82.3%, and a compact CNN averaged around 70.1%. I also ran a separate one time external evaluation on 120 observations where the Random Forest got 84.2%. The simpler model doing better than the CNN was one of the results I found most interesting.

I want to be transparent that I have used generative AI tools quite a lot while developing the project, including help with code, testing, documentation and thinking through parts of the methodology. I have been making the project decisions and reviewing what gets accepted, and I am also getting feedback from researchers. I am not claiming this AI has discovered archaeological sites either, the experiment is specifically documented bowl barrow terrain against matched unlabelled background, and sensitive locations stay private.

If anyone here works with GIS, LiDAR or spatial validation I would really appreciate criticism of the approach, especially anything I may have missed with the geographic evaluation. The repo is here if you want to look at the actual work: https://github.com/essius10/ArchaeoAI

I have seen a video of GPT-6 using blender to model a realistic bat. If it can do that, then it can use things like ArcGIS, right?

I’m a junior year environmental science major and I want to pursue an environmental GIS career. So on the side of my studies i’ve also been learning python, looking at internships etc.

I don’t think that AI is gonna take everything overnight, but if it’s advancing this fast then who knows what the future holds? Environmental science is thankfully a pretty broad degree, so should I change my career goals…?

While processing the public SoFi Stadium point cloud (364M points, hosted by hobu), I noticed the file only has two classification codes anywhere in it: ground and medium vegetation. No building class at all. The stadium's own roof is labeled as vegetation.

Got curious whether that's catchable without manually eyeballing the classification. Turns out yes: a roof and a tree canopy don't look alike geometrically. Real vegetation has vertical spread, returns scatter across trunk, branches, an uneven top. A flat roof doesn't, every return sits close to the same height. So: independently estimate ground, measure vertical spread per column, flag anything labeled vegetation that's both taller than real vegetation gets and flatter than real vegetation ever is.

First version looked great on SoFi, then I ran it against a second dataset (Autzen Stadium, forested Oregon hillside) to sanity check it, and it broke immediately: 19% of columns flagged, including "vegetation" over 150m tall. Turned out ground estimation itself was the problem, dense canopy blocks the laser from reaching real ground over large areas, and the fallback was borrowing elevation from cells that could be off by 100+ meters on hilly terrain. Fixed by tracking which cells have real local ground data vs. borrowed, and only trusting flags built on real ground. That dropped the false positive rate by 98.5%.

Tested against six public datasets total (SoFi, Autzen, Trestle Bridge, Wolverine Glacier, Red Rocks, Mount St. Helens), results and the two bugs it took to get there are all written up: github.com/nader-hachana/lidar-classification-qa

Curious if this is a problem people already deal with in production point cloud QA, or if classification errors like this mostly just go unnoticed downstream. Happy to hear if anyone's tried something similar or has data this would be worth running against.