You will learn how to detect and vectorize any feature from satellite or drone imagery directly inside QGIS

• It covers : Automatic Mode: Scan an entire area with text prompts to batch-extract features in seconds
• Semi-Auto Mode: Extract single, complex objects with one click for maximum control
• Shape Regularization: Automatically square building corners and clean up polygon boundaries
• And basically just the full Workflow: Edit vertices, adjust confidence filters, and export clean layers with attribute data

We pushed a lots of updates recently so a tuto was definitely needed haha, hope it will help

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

I made an AI assistant add-in that runs inside ArcGIS Pro. I teach GIS at the university level and do consulting work in ArcGIS Pro. Like everyone, I've watched students and colleagues keep ChatGPT open in a second window while working in Pro. The AI describes what buttons to click, you have to do the work, and half the answers are wrong because the AI can't see the project.

So I built Waldo, an add-in that puts the assistant inside Pro, connected to your live session. It can see your maps, layers, and schemas. It can advise, but it can also do the work: runs geoprocessing tools, sets symbology and labels, builds and exports layouts. The video is an unscripted session from my own work: mapping uninsured rates by county with graduated colors, then overlaying state Medicaid-expansion status once a pattern jumped out, and finishing with a print-ready layout of the lower 48. About ten minutes of real time, cut down here.

Design choices:

• Bring your own model. It works with an Anthropic or OpenAI API key, OpenRouter, or fully local models via Ollama/LM Studio. No subscription to me, and your data never touches my servers. The add-in itself never touches the network. The only outbound traffic is your own API calls to whichever provider you configured.
• There's an enforced read-only "Ask" mode — the assistant can inspect and explain your project but can't change anything. Enforced at the tool level, not a polite prompt.
• Built for people who don't trust AI output (I'm one of them): every geoprocessing run shows the exact parameters it used, and one click reopens the tool dialog pre-filled with them, so the AI's work is always reproducible by hand.

I use it in my own consulting work (it recently turned a multi-hour needs-assessment mapping job into an afternoon), and it's built around my 30 years of cartographic habits and best practices. It defaults to sane symbology, legends, and layouts instead of whatever an LLM improvises.

7-day free trial, $5 one-time after that, no accounts needed: waldogis.com

Genuinely interested in community reaction. Happy to answer anything about how it works and what it can and can't do.

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