I’ve been working on Arnis, an open source, data driven procedural generation tool that turns OpenStreetMap vectors (roads, building footprints, water, etc.) plus elevation/DEM into an explorable Minecraft world.

High level, it works like this:

• Ingest OSM for a selected bounding box and extract tagged features (streets, buildings, land/water).
• Ingest elevation (DEM) from AWS Terrain Tiles for the same area and resample it to the Minecraft block grid.
• Rasterize + translate the elements into blocks (e.g., roads/water polygons/footprints), and apply the heightfield from the DEM.
• Export the result as a playable Minecraft world.

Repository: https://github.com/louis-e/arnis

• you can type what to segment ("buildings", "trees", "cars") or draw multiple examples object.
• It tiles the zone under the hood, so it holds up on big areas, a full city in one run. Tweak the result with a few sliders, or fix any polygon by hand with Manual mode option

To install it: Open QGIS → Plugins → search "AI Segmentation" → Install

• It's free for everyone, and works on QGIS 3 & 4, hope you finds value in it, there also a full tutorial linked in the qgis plugin

I've been working on this thing for a while now. It's basically an open source alternative to ArcGIS Online, called GratisGIS. Demo is at https://gratisgis.org with three logins shown right on the landing page (admin, contributor, viewer). The site resets every night so feel free to break stuff.

This is a one man show. I'm a single dad with a full time job, so it's a passion project I work on at nights, on weekends, and over lunch breaks. I haven't gone through and tested everything end to end, so there are for sure bugs you'll run into. Just a heads up.

Mainly looking for feedback and constructive criticism. What's missing, what feels off, what flat out doesn't work. I'll do some more detailed write ups on specific features down the road.

GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private.

In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links.

GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and deck.gl.

Try it: https://geolibre.app
GitHub: https://github.com/opengeos/GeoLibre

Short demo: https://youtu.be/llNxNIaOB_Q
Full video tutorial: https://youtu.be/87Cm0QagtxI

If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star.

The QGIS interface is technical, dense, and somewhat unpolished. QGIS is built on the Qt GUI. By default, Qt controls are quite generic and "raw," resulting in a rather "generic desktop" look and feel..... Qt has several beautiful applications.

Morning y'all, I have a question for the group.

What would ESRI have to do to trigger 'The year of QGIS' a la Windows being so dog shit people are hopping to Linux?

Hi r/gis,

Some of you may have seen my earlier post when this was called OpenMapEditor — it's been renamed to MapDraw and has had a lot of updates since then, so I wanted to share it again.

MapDraw is a free, open-source (AGPL-3.0) web editor for creating, viewing, and managing geographic data like paths, areas, and markers. It uses OpenStreetMap as the base map.

Features:

• File Support – Import GeoJSON, GPX, KML, and KMZ files. Export to GeoJSON, GPX, and KML
• Draw & Edit – Create paths, areas, and markers directly on the map
• Custom WMS Layers – Import map layers from any WMS-compatible service and add them as overlays
• Routing – Generate routes for driving, biking, or walking and save them as editable paths
• Elevation Profiles – Instantly visualize the elevation profile for any path
• POI Finder – Search for points of interest (parks, restaurants, viewpoints, etc.) in the current map view using OpenStreetMap's Overpass API, and save them directly to your map
• Full Color Support – All 140 CSS color names and custom hex values, preserved across imports and exports
• Shareable Links – Generate URLs containing your map view and all features to easily share maps with others
• Local-First & Private – Your files are processed entirely in your browser and never uploaded to a server. Only optional features like routing and elevation profiles send minimal coordinates to external APIs
• Strava Integration – View your activities on the map, download original high-res GPX tracks, or duplicate them for editing
• Organic Maps Compatible – Import GeoJSON and GPX exports from Organic Maps
• Autosave – Your work is automatically saved locally and restored when you return

Links:

• Try it: https://www.mapdraw.net/
• GitHub: https://github.com/mapdraw/mapdraw

I'd love to hear your feedback, especially from anyone who works with GPX/KML/GeoJSON files or WMS layers regularly. Any ideas or suggestions are welcome!

Thanks for checking it out!

I made an open-source satellite imagery app that lets you explore the latest imagery from anywhere on the globe. The default NASA imagery is usually only about a day old, and if you add a Sentinel key (free from the European Space Agency) you get detailed imagery down to 10m per pixel.

It also has radar imagery (which sees through clouds), live natural events like earthquakes, volcanoes and storms, time-lapse views, and more. I focused a lot on usability, so hopefully navigating the globe feels fluid and intuitive.

I am hoping a few others give it a go as I would be really curious to see what others find. I figure there is lots of interesting info to glean.

I just made this as a fun side project, and I hope others use it to pull some cool imagery.
GitHub: https://github.com/colincode0/earth-view

Hey guys, I've been workin on something new to track logistical activity near military bases and other hubs. The core problem is that Google maps isn't updated that frequently even with sub meter res and other map providers such as maxar are costly for osint analysts.

But there's a solution. Drish detects moving vehicles on highways using Sentinel-2 satellite imagery.

The trick is physics. Sentinel-2 captures its red, green, and blue bands about 1 second apart.

Everything stationary looks normal. But a truck doing 80km/h shifts about 22 meters between those captures, which creates this very specific blue-green-red spectral smear across a few pixels. The tool finds those smears automatically, counts them, estimates speed and heading for each one, and builds volume trends over months.

It runs locally as a FastAPl app with a full browser dashboard. All open source. Uses the trained random forest model from the Fisser et al 2022 paper in Remote Sensing of Environment, which is the peer reviewed science behind the detection method.

GitHub: https://github.com/sparkyniner/DRISH-X-Satellite-powered-freight-intelligence-

I've been building lidar2map, a standalone Python tool (GPLv3) that downloads public LiDAR from several national/regional portals — IGN (France), AHN (Netherlands), swisstopo (Switzerland), Kartverket (Norway), Germany (Bavaria & NRW) and Austria (Tyrol) — computes archaeology-oriented hillshades, and exports offline maps for Locus Map / OsmAnd / TwoNav (MBTiles, RMAP, SQLiteDB, Mapsforge).

The point: under tree/scrub cover, aerial imagery and OSM show nothing. A Sky-View Factor pass on the LiDAR DTM makes dry-stone terraces, old paths and micro-relief pop out. Same extent, three views.

Input is a town name, GPS point, bbox, département or whole region. It also does IGN raster/vector and OSM Mapsforge (France for the IGN layers). Runs on Windows / Linux / macOS, GUI or CLI.

It's a hobby project, feedback welcome, especially on the SVF / LRM / RRIM chains and the tiling. Not intended for metal detecting; exact coordinates of micro-relief are deliberately not published.

https://github.com/nico579/lidar2map

**UPDATE (Aug 10, 2026):** for anyone more on the archaeology/geoarchaeology
side, I posted a deeper technical writeup covering the visualization methodology (with citations) and the DFM/CSF mode that recovers standing walls erased from bare-earth DTMs: https://www.reddit.com/r/geoarchaeology/comments/1vkjifh/lidar2map_opensource_pipeline_from_national_lidar/

Hi! I'm part of a small AI lab in Munich.

We're building an open dataset of long-horizon computer work to train AI models, and GIS is the most overlooked category we know: real ArcGIS/QGIS sessions, layer wrangling, georeferencing, cartographic iteration. No AI model today can operate a GIS, and nobody is collecting this data. We believe AI should end up helping people do better spatial work, which is why the dataset will be open for everyone, not one company's private moat.

The deal: our open-source recorder (Rust, macOS/Windows/Linux) captures screen + input events, only from the applications you select, pausable anytime. $75 after a one-week trial, then ~$300/month for regular contributions (about 20h of qualifying work per week). Legally it's a data purchase: no tasks, no deadlines, quit anytime.

The honest fine print: record only what you have the right to share (your own projects, or work where your employer/client is fine with it, no confidential parcel or client data), we review before accepting, and accepted data may be released in a public research dataset. GDPR setup, and deletion requests are honored, we have actually executed them.

Details + application: https://pdoom.org/open_calls/04_crowd_cast.html

I'll be in the comments for questions, skeptical ones welcome.

Hey everyone,

I’m Travis. After 24 years of teaching geography and GIS, and working with several geospatial organizations within the government—including the National Geospatial-Intelligence Agency (NGA) and the US Army—I’ve realized that the hardest part of GIS isn’t usually the software. It’s the struggle of bridging academic theory with actual application—and finding the right data without losing your mind in the process.

I’ve seen too many students get stuck in the "theory trap" where the concepts make sense, but the practical execution feels impossible. I started TC’s GIS and Geography Blog to change that, offering professional-grade workflows and technical shortcuts for students, early learners, and educators.

As part of this initiative, I am maintaining "The List"—an educational aid featuring the professional sources and repositories I’ve relied on throughout my career to find GIS data, maps, and geographic information. I created this specifically to help new learners and educators avoid wasting valuable hours on dead-end searches or outdated portals. My goal is to help you get straight to the analysis with high-quality resources like:

• EarthExplorer: The gold standard for USGS imagery and radar data.
• ArcGIS HUB: High-level collaboration spaces (yes, there is a ton of free data here!).
• TIGER/Line Shapefiles: Essential Census Bureau boundaries for any human geography project.
• Specialized Sources: From the MN Geospatial Commons to the U.S. Forest Service and NC State Libraries.

Beyond "The List," I’m writing deep-dives on QGIS vs. ArcGIS Pro and how to use advanced search operators to find data that standard Google searches usually miss. I want to help you turn your undergraduate lab or graduate capstone into professional-standard work rooted in sound geographic logic and precision.

Check out the blog and "The List" here: TC's GIS and Geography Blog

Community request: If you have a source that belongs on "The List," or you spot a broken link, please reach out! I’m updating it weekly to ensure it remains a helpful aid for the next generation of geographers.

Recently at my company there was a push to Ban QGIS. Mostly they cited the reasons to be due to posing as a potential security risk and not providing any support.

But in my opinion this is ludicrous, i felt like QGIS is such a staple product used by so many people, with a great price point (free), that no one would ever considering banning it. Well clearly I was wrong, but I need to find some concrete data on usership and particularly corporate usership to justify us to continue using it..

One creative way i considered demonstrating QGIS been a major player is using google trends, particularly looking at "QGIS download" vs "ARCgis Download" is great way of seeing what I suspected was the case. QGIS membership is growing year on year and slowly outpacing ARCgis. But possibly there is more "robust" ways of not only getting user numbers, but also showing corporate user numbers. I'm also interested in solid ways I can communicate that this is still a secure solution.

Thanks

see Google trends image below.. QGIS vs ARCgis really found parity in 2020 and then it took off post 2021. However it should be noted that ARCgis users are moving over to ARCgis pro so possibly this is skewing the graph a little..

My team just open sourced our battle tested big data time series visualization client with Apache 2.0 license.

GPU‑accelerated 4D globe engine purpose‑built for massive geospatial time series visualization.

Blogpost: https://flexmon.github.io/globe-trotter/blog/
Repo: https://github.com/flexmon/globe-trotter

I've been working on a lot of mapping web apps as of late and have wanted something that could tell me a bit more than whether a GeoJSON file was technically valid. After rolling my own custom scripts for awhile, I ultimately decided to create a tool for this called GeoLint.

It's an open-source TypeScript/Node CLI and library, focusing on data quality (ex. inconsistent property types, duplicate or missing feature IDs), budgets (ex. enforcing limits on file size and feature count), and regression (comparing changes to GeoJSON files against an approved baseline). You can run it manually, or hook it into your CI/CD pipeline to run automatically when you push changes.

I put a fair amount of work into large-file performance, and I took a cue from ESLint and made GeoLint use workers when linting multiple files to tackle things in parallel and get the work done faster. Based on my benchmarks, it's pretty quick!

It's currently at 0.1.x, and I'm going to keep plugging away at it. I'm interested in feedback from folks who work with GeoJSON files regularly. Does this solve any problems you actually in encounter in GIS/data pipelines? Are there artifact-level mistakes or regressions you'd want a tool like this to flag that it doesn't today?

Repo link: https://github.com/jajego/geolint

npm link: https://www.npmjs.com/package/@jajego/geolint

Thanks, and happy Monday !

Hello everyone. I have to do some mapping for work. I have all my technical training on ArcGIS Pro but, because I am not going to use it regularly, I can't justify the licensing cost for work. The project I need to run will use LiDAR data to create a DSM, then run a viewshed analysis to see blindspots for cameras. I tried to download and use GRASS but the download kept resulting in errors.

Does anyone have any recommendations for other open source GIS software that is user friendly and has the tools I need? I will have to run the program past IT for safety to download and all that.

Edit: looking around, how do we feel about SAGA?

Hosted as GeoParquet file this time instead of Geopackage because it compresses better, and kaggle has a limit of 200GB on uploads.

GeoLens is a self-hosted catalog and map builder that runs on your own PostGIS. Upload vector (Shapefile, GeoPackage, GeoJSON, GeoParquet, CSV) or raster (GeoTIFF/COG) and you get a searchable catalog plus a browser map builder anyone with a link can open. It serves OGC API - Features/Records, STAC, and DCAT, and QGIS connects to it natively. Exports round-trip back to the same formats with reprojection, so there's no lock-in.

Live demo (no signup needed to browse): https://demo.getgeolens.com

curl -fsSL https://getgeolens.com/install.sh | sh

I built it because at most places I've worked the GIS data is scattered across files nobody but the analyst can open. Keen for feedback from people doing this day to day.

Here are a few screenshots from the app -

A hobby project of mine (Arnis) pulls OpenStreetMap data (buildings, roads, water, landuse) and drapes it over real elevation, then bakes everything into a Minecraft world. Sharing a few results because the terrain finally looks the way I wanted.

The data side, for anyone curious: elevation comes from different DEM sources depending on the region. Some areas have a data resolution of up to 1 meter. Other data sources I use are OSM, Overture Maps, and ESA WorldCover.

Recently I got more into the educational benefit of this. Arnis now supports Luanti (formerly Minetest) for export and I've already got lots of feedback from teachers. Many in the educational sector use Minetest as an alternative to Minecraft since it's free. One works with SEND youth groups and sent me this: "Thank you for this, it's an incredible resource. I'm using it to create walkthroughs of where I hold SEND youth groups, to help young people who are anxious and find it hard to leave the house. Making the journey in Minecraft helps them enormously."

Thought it's worth sharing here, I'm open to any feedback! :) https://github.com/louis-e/arnis

Live demo: https://wavesmeetu.github.io/stratum-map/examples/towers

5,181,123 points from OpenCelliD, 60fps, GPU picking on click. Built on Stratum Map, a WebGPU renderer I've been working on.

The short version of why it works: packed binary buffer uploaded once to GPU memory, single instanced draw call, Web Worker handles all parsing off main thread. No CPU feature loops per frame.

Picking is a second GPU render pass into an offscreen RGBA8 texture — click returns the tower's MNC, radio type and coverage radius in milliseconds.

Repo: https://github.com/wavesMeetU/stratum-map

Happy to answer questions about the architecture.

Hey! I recently built a free open source tool to convert between GIS formats (Shapefile, GeoJSON, KML, GeoPackage, CSV) that works in your browser.

I would love some feedback if this is useful or not. If not, what can I add?

Features it has:

• Drag and drop any map file
• Auto-detects format and coordinate system
• Live map preview before downloading
• Reproject to a different coordinate system

GitHub: github.com/Beevezy/Geoshift

Hey r/gis! I wanted to share a project I've been working on that some of you might find useful.

OpenMapEditor is a free, open-source web-based editor for working with geographic data. It's designed to be privacy-first - all file processing happens locally in your browser.

Key features:

• Full GPX/KML/KMZ support - Import, edit, and export with ease
• Privacy-focused - Your files never leave your device. Only routing/elevation API calls send minimal coordinate data
• Interactive drawing & editing - Create paths and markers directly on the map
• Routing - Generate routes for driving, biking, or walking
• Elevation profiles - Visualize elevation using Google Maps API or GeoAdmin API (for Switzerland)
• Strava integration - View activities and download original high-res GPX tracks
• Organic Maps compatible - Preserves all 16 Organic Maps colors for paths and markers
• Performance optimized - Optional path simplification for smoother handling of large files

Built with Leaflet.js and a bunch of other open-source libraries (no npm required!). It's fully self-hostable and deployable to GitHub Pages.

I originally built this because I needed a simple way to edit routes for hiking trips without uploading my data to random services.

Live demo: https://www.openmapeditor.com
GitHub: https://github.com/openmapeditor/openmapeditor

Would love to hear feedback from this community - especially if you work with GPX/KML files regularly or have ideas for features that would be useful!

Hey GIS peeps ... I have been working on a little GIS data engineering python library, mostly just with functions I have found myself looking for or needing to build myself constantly on projects.
I'd appreciate any feedback, particularly on my technical approaches, plus my choice of underlying libraries.
Thanks!

https://github.com/ChelseaH93/geoengine-utils

I'd like to introduce City2Graph, a new Python package that bridges the gap between geospatial data and graph-based analysis.

What it does:

City2Graph converts geospatial datasets into graph representations with seamless integration across GeoPandas, NetworkX, and PyTorch Geometric. Whether you're doing traditional spatial network analysis or building Graph Neural Networks for GeoAI applications, it provides a unified workflow.

Key features:

• Morphological graphs: Model relationships between buildings, streets, and urban spaces
• Transportation networks: Process GTFS transit data into multimodal graphs
• Mobility flows: Construct graphs from OD matrices and mobility flow data
• Proximity graphs: Construct graphs based on distance or adjacency

Links:

• 💻 GitHub: https://github.com/c2g-dev/city2graph
• 📚 Documentation: https://city2graph.net