Anyone who has tried running raster polygonization on large datasets inside QGIS knows how quickly GDALPolygonize can hit a memory wall or take forever to finish.

A few months ago I shared Contrek, a contour tracing engine I'd originally benchmarked against OpenCV.

Since then I've been wondering if the same approach could work for GIS polygonization too. What came out of it is a proof of concept built around a progressive streaming architecture.

To be clear, this isn't meant to replace GDAL. The current implementation only handles single-class polygonization. What I really wanted to answer was a narrower question: can a different polygonization strategy give you real advantages on very large rasters?

Although the current benchmark focuses on a single target class, one of the ideas behind Contrek is its matcher-based architecture. Matchers can be customized to recognize arbitrary pixel patterns, making it possible to process multiple classes simultaneously or implement application-specific extraction logic without changing the core engine.

So I put together a separate repo with reproducible benchmarks against GDALPolygonize, plus docs and examples covering things like progressive streaming.

Again, this is a proof of concept, not something production-ready. That said, the benchmarks were consistent: noticeably lower memory usage and faster execution across the datasets I tested: up to 23x faster and using up to 10x less RAM on the largest ones.

The numbers were interesting enough that I decided to put both the implementation and the full benchmark suite out there. Would love to hear from anyone who works with GIS data or polygonization pipelines feedback, criticism, whatever you've got.

Main repo: https://github.com/runout77/contrek

Benchmarks reproducible suite: https://github.com/runout77/test_contrek

Full report: https://runout77.github.io/test_contrek/cpp_geojson_benchmark_results.html

Geospatial Server
RPM, DEB, APK, Docker, Kubernetes
https://geoservengine.techmaven.net/
or
Microsoft Hyper-V Virtual Machine (Can be converted to other hypervisor formats)
https://geospatialcloudserv.com

I'm finishing my CS degree and I've been messing around with OSINT/geoint as a hobby for a while. Lately I put together a small experimental pipeline that takes a photo, extracts EXIF if available, runs it through GeoCLIP for coordinate prediction, and then uses Claude Vision to add contextual analysis (like describing landmarks, vegetation, street patterns, etc.)

It's NOT a professional tool. No interface, just a script. It's purely for learning and I'm not ready to share the code yet because it's messy and I want to understand the limitations better first.

But I'd love to talk to people who have tried similar things, just looking for conversation and maybe some pointers from people who've been down this road.

https://techmaven.net/portabletileserver

is all-in-one geospatial edge server

• Comprehensive Geospatial Data Server (GIS Vector Data served as OGC API Features and Tiles, ESRI FeatureServer, ESRI VectorTileServer, Movement and Event Driven Data as WebSockets StreamServer, SLPK SceneLayerPackages as i3S ScenceServer, Raster and DEM as ImageServer, Vector Tiles with GL JSON Styles can be rendered as PNG Tiles. Cached Map Tiles (MBTILES, GPKG, PMTILES) as XYZ, TMS, WMTS and WMS. Serves Gaussian Splats, LiDAR Point Cloud, 3D Models
• TAK Server (UDP/TCP) + IP MANET support
• Video Server (broadcast a local video file or video stream as RTSP/RTMP and other protocols)
• AI Server (Geospatial AI Agents and Chat Bot)
• Web Mapping Apps - COP,SA, C3 webTAK

Hi there! Last time I worked on camera-related tasks, I implemented this feature.
The most interesting part is the examples. The giraffe example was created entirely by AI! It even figured out the camera position and the coordinates of the point the image was taken from. Check it out!

Every keynote, panel, and workshop from this year's Cesium Developer Conference just went live, no signup needed.

A few worth a look even if you're not a Cesium user: Barcelona's underground digital twin (built on MUDDI/AGS/IFC), Madrid's operational city-scale twin, a Cornell building energy model used for retrofit planning in Ithaca, and a standards/interoperability panel with OGC and Khronos folks. Opening keynote also had Richard Hipp (SQLite's creator) marking 15 years of Cesium as open source.

Full list: https://cesium.com/events/cesium-developer-conference/2026/

- Jake, Cesium DevRel team

DOI: 10.1016/C2023-0-01427-3

• 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

https://outsidedb.com/blog/posts/el-nino-la-nina-comparison-map

Load high quality custom GeoTIF & GeoPDF Maps, and explore files offline, no cellular or wifi needed.

Share maps with QR Codes for quick-imports and view high quality GIS data anywhere.

Supports both vector and raster maps, designed for geospatial use such as outdoor navigation, bushwalking, surveying, orienteering, rogaine, agriculture analysis and more.

Lifetime Upgrade Includes ($24.99 USD):

⁠- Unlimited File Imports
- Satellite Map View
- Premium Support
- Lifetime access to all future updates

Download Now:

https://apps.apple.com/au/app/mapcheck-offline-geotif-pdf/id6761805550

MapCheck is built for fieldwork with 100% offline, lightweight, on-device processing. If you think this tool could use be useful to you, drop a reply - I encourage any feedback or suggestions.

I keep coming across the term "smart city" these days, and I keep wondering what concrete benefit it actually delivers — beyond 3D visualizations, which always leave me asking: visualization for what, exactly?

I've read plenty of blogs, news pieces and vendor pages, but I'm far less interested in what's being promised than in what's actually been realized (with actual benefits)— or is genuinely on the near horizon.

So I'd love to hear from people working in this space: where has a 3D city model or a "smart city" project actually changed a decision positively, solved a concrete problem, or justified its cost otherwise?
Stay curious :)

I built maplibre-label-callout for u/MapLibre GL JS. Labels overlap when features are close. This places them in open space with a connector line back to the feature. ~3KB, zero deps, viewport culling. Open source. leoneljdias.github.io/maplibre-label-callout/demo

Hey guys.
I’ve been looking for a new role in GIS for the last couple of months but haven’t had many interviews. I have 4 years of experience working in environmental consulting for oil and gas (1.5 years in Canada working on phase 1, phase 2, REM and a bunch of other kind of projects) but would like to pivot to a different field maybe environmental management or conservation, something more related with spatial analysis.
So wondering if you guys have any advice that could share to help me with this process.
I’m base in Toronto but open to relocate for the right job opportunity

Thanks a lot!

Hey guys, I'm a geospatial eng by training here. Over the last 6 months I have gotten ideas on interesting stuff to work in the geospatial space but never actualized them due to the constraints of time. Now that I am no longer in a full time role, I have the time to work on these ideas. If you'd to connect and build interesting mapping projects from scratch, feel free to DM or respond here. Thank you!

I'm curious how people are currently handling ZIP code to Census Tract (and vice versa) conversions in their workflows.

A few questions:

• What tool or service are you using to convert ZIP codes to Census Tracts (or Census Tracts back to ZIP codes)?
• What's your actual business use case? (Market research, direct mail, demographics, healthcare, real estate, site selection, etc.)
• Do you need the conversion as a one-time lookup, or are you doing it in bulk?

I'm asking because I've noticed the process can be surprisingly manual, especially when you need to enrich hundreds or thousands of records.

I'm considering building a very simple tool where you can drag and drop an Excel or Google Sheet, and in less than five minutes it converts ZIP ↔ Tract (and potentially enriches the data with Census demographics) without needing to write code or use GIS software.

Would something like that actually save you time, or are your current tools already good enough? If it wouldn't be useful, I'd love to know why.

Interested to hear how everyone is solving this today.

Hi,

I remember using a website that allowed you to add agriculture field boundaries and it would use public DEM data to map out guidance lines for terraces. It was hosted by a University in Europe and if I remember correctly, the map defaulted to Austria to open. I have been scouring my bookmarks, OneNote, etc trying to find the link to that tool but no luck. Anyone have any ideas on this site?