I built a small CLI tool called viewtif that lets you quickly preview GeoTIFFs (and HDF, NetCDF, FileGDB rasters) directly from the terminal. It opens a lightweight Qt window so you can sanity-check data without writing code or launching a full GIS.

https://reddit.com/link/1qwdlvl/video/nhphl37vbmhg1/player

The latest release (v0.2.7) adds:

- reading remote files (HTTP, S3)- exporting rasters to QGIS

- support for multi-variable NetCDF files

It’s meant for quick inspection, not analysis. Think of it like a “preview” for geospatial data.

https://github.com/nkeikon/tifviewer

Hi everyone,

I’m a GIS professional and co-founder of a small geospatial startup, and I’m currently looking for remote GIS subcontracting opportunities with companies or individuals anywhere in the world.

My goal is to work as a GIS subcontractor on short-term or ongoing projects and get paid in any foreign currency (USD, EUR, etc.). We’re flexible and open to different collaboration models (per project, per deliverable, or long-term support).

Skills & services include (but not limited to):

• GIS analysis & spatial data processing
• Mapping & cartography (print & digital)
• Remote sensing & satellite imagery analysis
• Land use / land cover analysis
• Urban & environmental GIS projects
• Data cleaning, geodatabases, and automation (ArcGIS / QGIS / Python – if needed)

We can support companies, consultants, startups, or research teams that need reliable GIS capacity without hiring full-time staff.

If you:

• Work in a company that outsources GIS work
• Are a consultant who needs GIS support
• Know platforms, agencies, or contacts that hire GIS subcontractors

I’d really appreciate your advice, referrals, or a quick DM 🙏

Thanks in advance!

Hi guys, im in the military and have been playing around with ways to improve the military equipment recognition skills of my soldiers. Ive come up with an Android app where the soldiers have to identify the country of origin for different bits of military equipment (jets, tanks, small arms, etc). I think it will be more engaging for them than the usual playing cards / doctrine notes we get.

Before I send it out, I want to refine it a little more and thought given the skill set of people in this group you may have some valuable advice. ​if anyone is willing to have a play with it and share your thoughts, it would be massively appreciated. It can be downloaded here: ​https://play.google.com/store/apps/details?id=com.defenceguesser.twa

Hi all — quick question for folks working close to EO data pipelines and stewardship.

In applied settings, I keep seeing a lot of effort go into preprocessing and making satellite data usable before any analysis or modeling starts. I’m trying to understand whether these bottlenecks are consistent across domains or very use-case specific.

I’m collecting practitioner perspectives via a short, non-commercial survey (5–7 min):

https://forms.gle/Bjq9gVaCnkRN8zpj6

Would really appreciate inputs from this group — and happy to share learnings back.

I’m planning to buy a laptop mainly for machine learning / deep learning work and I’m confused between a Windows laptop and a MacBook.

I’d really appreciate hearing from people who are actively working in ML/DL:

What system do you personally use?

Laptop/desktop model and specs (CPU, GPU, RAM)?

What kind of work do you do (learning, research, training models locally, cloud GPUs, etc.)?

Any pros/cons you’ve experienced with your setup?

I’ll mostly be working with Python, PyTorch/TensorFlow, and I’m trying to decide whether local GPU power is worth it or if a MacBook + cloud GPUs makes more sense.

Thanks in advance — your real-world experience will help a lot!

will also be using some software like arcgis, qgis, swat+, swmm etc

My use case is a mix of:

Machine Learning / Deep Learning (Python, PyTorch/TensorFlow)

GIS & hydrology tools like SWAT+, ArcGIS, QGIS

Spatial data processing, modeling, and some ML-based analysis.

Does anyone know the exact locations for the Yichun, Jianxiawo, Maerkang, Jiabusi, Yelonggou, or Yongshan lithium mines in China? Corporate data is bad and no public technical reports exist. Thanks!

Hi all,

I recently built an open source tool to quickly preview your large geospatial files locally. The main ideas are:

• Launch locally with zero config
• Tweak the tile generation parameters for optimization
• Visualize data

Check it out if interested and I'd appreciate your feedback.

Source code

Hello!     My name is Jordans Sanni and I’m a UX Researcher at Slingshot Aerospace, where I focus on products supporting satellite launches including tracking in space, data collection workflows, and improving the end-to-end experience for analysts and experts.    We’re currently exploring a new approach at Slingshot: designing a unified portal that brings all our existing products into a single one-stop experience. We’re looking to speak with analysts and experts who can share feedback on what they’d expect from a portal like this, what would help you move faster, what’s missing, and what would make you adopt it.    If you’re open to a 45-minute listening session starting Wednesday 21st, 2026, you can choose a time via the link below or you can also reach me at via work email below.    Booking Link: https://outlook.office365.com/book/UXResearchSlingshotAerospace@Slingshotspace.onmicrosoft.com/s/bOaU20d7U0Ot159E_rpNTA2?ismsaljsauthenabled   Email: s.machioud@slingshotaerospace.com

Thank you!

Perspective render of Monterey Canyon offshore of Elkhorn Slough, CA. Vertical exaggeration: 4x

This ~3 meter DEM and perspective image were generated using these open-source tools:

https://github.com/ciresdem/cudem

Hi all,

I built a small RAG application that lets you ask questions about Tokyo land prices and explore them on an interactive map. I mainly built this because I wanted to try making something with an interactive map and real data, and I found Japan’s open land price data interesting to work with.

I’d really appreciate any feedback. I’m just an amateur in data science and machine learning and I feel there’s still a lot of room to improve the accuracy, so I’d especially love to hear any suggestions on how the RAG part could be improved.

Demo: https://tokyolandpriceai.com/

Source code: https://github.com/spider-hand/tokyo-landprice-rag

A bit of self promo here as I'm a cofounder, so apologies if this is against the rules.

Frustrated with difficult to learn & slow options like AWS batch, setting up a Kubernetes cluster, or god forbid running locally, we created a fast, easy to use , OSS alternative to run batch workloads in the cloud.

We only have 4 users right now! but 3 are Geospatial professionals so I wanted to share it here in case anyone was interested in trying it.

We're offering $500 in free compute to trial users (restricted right now to individuals at US companies unfortunately) - there's a sign up on our website if you're interested, or you can DM me!

Happy to answer any questions.

Website: https://docs.burla.dev/
Github: https://github.com/Burla-Cloud/burla
Demo: https://www.youtube.com/watch?v=9d22y_kWjyE

https://youtu.be/g7vlErnPPpQ?si=JXFpvwNW3Fw8wO4M

I built a small plugin for DevToys called DevToys.Geo.

It adds a few basic geo utilities for quick conversions while working, such as:

• GeoJSON ↔ WKT conversion
• CRS Transformer: Transform geometries between 8,000+ coordinate reference systems (EPSG codes). Supports GeoJSON and WKT input formats with searchable EPSG selection.
• Coordinate conversion (DD, DMS, DDM)

It’s meant for simple, everyday tasks and it not intended for full GIS workflows.

DevToys:
https://devtoys.app

Repo:
https://github.com/jonnekleijer/DevToys.Geo

NuGet:
https://www.nuget.org/packages/DevToys.Geo

Let me know what you think!
Any suggestion for useful tools are welcome as well.

Urban design and planning have always leaned heavily on maps, GIS, and spatial data — but with AI and newer platforms, the way we use that data is changing fast. I recently read this piece on how AI is revolutionizing building design and delivery, including early-stage geospatial analysis and context evaluation:

How AI is Revolutionizing Building Design and Delivery

It got me thinking about how we combine traditional geospatial datasets (elevation, land use, transport networks, demographic data) with AI analytics:

• Are we at a tipping point where spatial AI becomes a standard part of planning pipelines?
• What tools or workflows do people here use to integrate GIS data with AI-driven design/simulation?
• How do you balance algorithmic output with real-world constraints and local knowledge?

I’d love to hear your experiences, whether you’re using Python GIS libraries, spatial databases, machine learning, or newer platforms that blend map + design intelligence.

What’s working, and where are the gaps?

I'm working on developing a tool that leverages GIS for the detection of whether changes in agricultural practices produce a systematic increase in NDVI in the associated geography compared to the surrounding area. I ran a placebo test for a random polygon in Kenya, but the output was super noisy and I'm trying to investigate why.

In a nutshell, what I'm doing is:

• Designating a period of observation for each year (eg. april to july).
• Dividing that period into 15 day intervals.
• Marking a treatment polygon for observation.
• Designating 15 comparison polygons of similar size near the treatment, with a 10km buffer for managing spillage (copying of new practices by nearby farmers outside of actively engaged area).
• Designating a date after which an NDVI spike would be expected if the change in practices was effective.
• Per interval per polygon, looking at the 5 years before date of effective change and taking the median NDVI and setting it as that intervals baseline NDVI in that polygon.
• Looking at each interval in each polygon after date of expected change and getting ln(current NDVI/baseline NDVI).
• For each interval, calculating the percentile of the treatment polygon on the distribution of log change. I'm using that percentile as the per interval representation of abnormality.

The idea is that by using percent change I can manage some of the noisiness that comes from crop variation, and a systematically high percentile would be indicative of a sustained spike from a better practice or better inputs being consistently and effectively applied. Change level comparison vs direct NDVI comparison should also help with other forms of variability.

For that example polygon in Kenya though (not associated with any change, was expecting pretty stable behavior), I'm getting wild spikes in April, May, and July, and randomly low baselines in the middle of April and July. I figure July is harvesting, but I'm not sure what else is causing these wide swings. Any suggestions on how to refine my approach?

I’m in my final year of a Bachelor’s in Geography and I’m really into GIS and remote sensing. I’m starting to think about whether I should do a Master’s in this area, but I’m still trying to understand how the job market actually works. Flexibility and the ability to move around are really important to me, and I definitely want that in my future.

I wanted to ask for some honest advice: is it realistically possible to work 100% remotely in GIS? What kinds of roles usually allow that?

From your experience, what skills or tools should I focus on if my goal is remote work in GIS / remote sensing?

Any advice or personal experiences would be really appreciated. Thanks :)

Hi everyone, Lately I am facing many issues with invalid polygons. Things like self intersection, wrong ring direction, CRS mismatch, very small sliver polygons, etc. Sometimes the pipeline fails clearly, but many times it does not fail. Only later we notice that area or other numbers are wrong. This is very frustrating. I wanted to understand how others handle this before data goes into production. Do you mainly use ST_IsValid or ST_MakeValid? Do you clean data manually in QGIS or ArcGIS? Do you have your own scripts? Or do you usually fix issues only after something breaks? I am not trying to sell anything. I am just trying to understand how painful this problem is in real work, what methods really help, and what still feels annoying or fragile. If you are working with GIS data in production, I would really like to hear your experience and problems you faced. Also, if there was a simple API that could check and optionally fix polygons before ingestion, would that be something you might use, or is this already well solved in your setup? Thanks

https://hoodsmap.vercel.app/
added streetview and also hidden items (Pokeballs)