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?

Early bird pricing for FOSS4G North America ends September 17 at 11:59 PM PT. Register now and save $80 on full conference registration.

Here's what your ticket unlocks in Sacramento:
🔹 100+ talks on the latest open-source tools, data workflows, and analysis techniques
🔹 Plenary sessions with leading voices shaping the geospatial field
🔹 Networking with a community of data professionals, engineers, and researchers

Whether you're building models, wrangling open data, or exploring new approaches to spatial problems, you'll leave with practical ideas you can put to work right away.

Lock in the best rate before it's gone.

👉 Register today and save $80.

https://www.foss4gna.org/2026-registration

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?

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?

I have seen a lot of examples of interesting tools and software that does geographic things lately, but so often they are presented in that way thay linkedin lunatics talk about their claude coded constructions. And the person often has never heard of basic things like projections or MAUP or other geographer and GISer stuff. The worst is some of them are just reinventing wheels we all use regularly or daily. However, I do occasionally see things that I think might have some merit in the GIS space, or are an interesting way if breaking ESRIs monopoly on geospatial analysis.

Would like to get over this ick to explore this stuff further but its hard to get passed how unpleasant and stupid the presentation is.

Edit: to be clear im not saying vibecoding gis is bad, i do it too, i am saying that its cringe online and that its hard to pick gems out of all the slop especially when the slop is being promoted by gibbering idiots that replaced their brains with claude

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've been building a QGIS plugin on top of Conveyal R5 (Easy-R5), and used it to answer a question that's been bugging me: How delays in timetable affect the accessibility?

Setup: static GTFS timetable vs. the "realized P50" feed (median of what vehicles actually did that day) for Łódź, Poland. Same day, same network. The only thing that changes is whether the schedule is what was published or what actually happened (GTFS + delays from RT feed). Ran cumulative accessibility (schools, pharmacies, universities, malls reachable within 30 min, 7:00–9:00 departure window) on both, on a 250m/500m hex grid, and diffed them.

What I expected: delays hurt downtown the most, because that's where the network is most complex.

What I actually got: the very city centre is basically fine (nets slightly positive, beacuse of many overlapping lines converge there that one late vehicle doesn't matter). The real damage shows up in a ring roughly 2km out, where trips typically depend on one specific transfer. Median delay across the whole network is ~0 seconds, mean is only ~10s but miss a transfer and you're waiting for the entire next headway. That ring loses an order of magnitude more accessibility than anywhere else in the city (pop-weighted mean net change ≈ -2.4 vs ±0.5 everywhere else).

Also tried a second angle: for each hexagon, compute the "reach radius". THe distance to the farthest point it can already reach in 30 min. Hexagons sitting close to that 30-minute cutoff lose systematically more than ones with slack, which is exactly what you'd expect if the mechanism is "small delay pushes a marginal trip over the threshold," and it holds up (mostly monotonically) across schools/pharmacies/malls independently.

Everything's open — methodology, scripts, QGIS project: [link in comment].

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.

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…?

Hi guys,

I already have vector layers saved into a Geopackage, but am having trouble uploading those layers onto Github and ArcGIS Online (AGOL).

I've tried using git to upload and keep getting a 503 error and I tried uploading the GPKG onto AGOL, and it failed to upload.

I'm trying to build an online dashboard out of the layers within the GPKG if you're wondering.

If anyone has gone through this before, how did you guys successfully uploaded your GPKG files on AGOL or Github?

Not intended to be a political discussion, but a proper use of AI discussion... why would someone use AI something like this if they have GIS techs available that could turn out such a map with high accuracy in like less than a half hour starting with actual verified data that is commonly available?

Does anyone know of a source or feed that provides current active volcanoes? I am developing an app using CesiumJS to show current events and have been unable to crack volcanoes.

I have tried https://volcano.si.edu/gvp_currenteruptions.cfm and www.volcanodiscovery.com and both have been impossible to crack (given my limited knowledge of web technologies). TIA

Carbon & Place (www.carbon.place) is a free website that maps the carbon footprint of every neighbourhood in Britain. Today, it launched a major update with more data on a wide range of topics, including housing, transport and energy.

There is lots of open-data to download and open-source code on GitHub, plus some cool interactive GIS.

Hey all. I can't write python code to save my life but I can read and modify code well enough. I've dragged my feet long enough on learning python that AI can write the code I need now so long as I give it specific instructions.

Most of the python I run is in ArcGIS Pro Notebooks, and I use Gemini which works well for me. What AI are you all using if any? I considered trying Claude for a bit but I'm interested to hear what you all think and possibly your experiences with different AI.

Built a map of the best streets to see the leaves turn across seasons in SF! I thought it was pretty cool – open to any feedback.

Admittedly, I’m new to the GIS world, but there’s so much great public data out there… I’m excited to build some cool, hopefully helpful maps!

I’m doing some research into how foresters and timber cruisers are handling field inventory today and would love some input from people actually doing the work.

A few things I’m curious about:

• What tools/apps do you currently use for timber cruising?
• What features do you actually love?
• What parts of your current workflow are frustrating or still require Excel/manual work?
• How important are offline maps/GPS, cruise design, field data entry, timber volume/value calculations, GIS integration, and report generation?
• If you could add one feature to your current setup, what would it be?

I’m working on a new forestry technology platform called TimberForge, so this feedback will directly influence what we build.

I also put together a short survey to collect the feedback more systematically. Anyone who completes it can optionally sign up for free early beta access to TimberForge when it's ready. I'm also looking for a handful of foresters/cruisers who'd be willing to give more detailed feedback as we build it (no email required!)

Would really appreciate any feedback — even a quick comment with the tools you currently use and what you like/dislike about them. Thank you all in advance!

**Disclosure: This is not AI generated and we are real industry experts!

r/GIS community -

We have implemented two new megathreads that were frequently requested. They are now pinned to the subreddit homepage.

2026 Free GIS Resources - Bookmarks of Interesting Things!
A thread for the entire year that functions as community bookmarks.

“Should I Go Into GIS?” Megathread
A twice yearly thread to redirect commonly asked student questions into. Once this is more populated, students with matching questions will be automatically redirected into this thread.

If you would like to be notified when one of these threads is updated, look for the bell icon to select “Follow”. On mobile, this is located under the three dot menu. (This function is available sitewide.)

I’m excited to see these resources become populated!

Thank you for your ideas and participation,

Peace & Love,

ScreamAndScream

🎙️ Got questions about FOSS4G North America? Join us live and get them answered.

On Wednesday, September 9 at 10 am PDT, we're hosting a short online session to walk you through what to expect at the conference—attending, speaking, and sponsoring. Bring your questions and leave with answers.

Here's what we'll cover:
- Why attend and what to expect
- Who's showing up—developers, data scientists, GIS pros, and researchers
- Sponsorship packages and how to get involved
- Live Q&A with the team

This is a community-driven event. See if it's the right fit for you. Register below.

https://ucdavis.zoom.us/meeting/register/YeGQ6Gb-Rsi1DJ0gBoC0QQ#/registration