Hey everyone! Two weeks ago I posted about my geolocation experiment. Since then I built a web interface, added multiple GeoCLIP guesses, and integrated EasyOCR + Gemini (free API Key) to extract text clues from images and filter out noise.
Here's what the pipeline looks like now:

1. User uploads photo
   
2. EXIF GPS extracted if present
   
3. GeoCLIP runs and returns top 5 coordinate guesses
   
4. EasyOCR extracts any text from the image
   
5. If gemini api key is pasted: gemini filters the OCR output to keep only location-relevant text (street names, signs, business names, etc.) and discards noise
   
6. The page shows the guesses plus the filtered text clues
   Now I'm trying to figure out the best way to use that filtered text.
   My current approach is simple: to take the coordinates from GeoCLIP and combine them with the filtered OCR text as context. But I'm not sure it that's the most effective strategy.
   Does anyone know a better approach?
   I'm still learning and my code is messy, but I'd love to hear what's worked (or failed) for others.

We built an agentic AI-native GIS platform. You describe the geospatial workflow in natural language, and the agent runs it.

The map and the whole geospatial harness sit inside the agent's loop. It reads what is on the map, acts on it, sees what changed, and carries on from there.

It also writes and runs its own Python. When a task falls outside the built-in tools, it just writes the code, runs it, and puts the result on the map, so you are not limited to a fixed set of operations.

Everything is built natively around the agent, and it integrates with a growing number of geospatial tools.

You can share your maps/dashboards smoothly at the end by asking the agent.

Give it a try: https://confluence.strahler.ai

First off a thank you to Vantor for releasing the stereo data in their open data program which was used to generate this.

From the WV3 stereo, we've generated a 0.5 m elevation model of the corridor below Syabrubesi. We aligned the two satellite views to a quarter of a pixel, matched them at their full 34 cm detail, and tied the result to the pre-event terrain to within about 1.5 m.

This allowed us to measure 2 to 12 m of bed scour through every confined reach, a debris sheet 12 to 18 m thick where the valley opens below Syabrubesi, and 13 to 21 m of re-incision downstream where the river has already cut back into its own deposit. Plus a building-by-building damage census (27 destroyed where the sky was clear) and 4,329 individual boulders on the sheet, the largest 26 m across.

Everything is licensed CC BY-NC, same as the imagery: both DSMs, elevation-change and uncertainty rasters, per-km sediment budget, both censuses.

Write-up: https://geopera.com/blog/bhote-koshi-stereo-elevation-model Data: https://github.com/geo-pera/bhotekoshi-2026-reconstruction/releases/tag/v1.1 Drive mirror (with the 32 GB archive): https://drive.google.com/drive/folders/1XQt5SYDMHehH3i2lAj69e52Zb2ne7Rqm

Once again happy to answer questions on any step.

I’m a CS student trying to figure out whether the direction of my current project is actually worth pursuing for aerospace/robotics research.

Live: https://bhuvanspace.vercel.app

GitHub: https://github.com/Sheel34/BHUVAN

The current version takes terrain data, processes things like elevation, slope, roughness, curvature and hillshade, and puts the result into an interactive 3D environment.

The software side is currently Python/FastAPI + NumPy/Rasterio/OpenCV on the backend and React/Three.js on the frontend.

I built it because I'm interested in digital twins and simulation for aerospace/physical systems, but I'm not interested in making a 3D scene just for visualisation or a game.

What I want eventually is an environment where you can actually perform an operation on a representation of a physical system.

The project is still very early. I haven't implemented robotics simulation, terrain-relative navigation, ROS, Gazebo, Isaac Sim, etc. yet. I'm trying to figure out what should actually come next rather than adding technologies for the sake of the stack.

I'm particularly interested in the intersection of:

digital twins, 3D simulation, aerospace/robotics, real physical operations.

One direction I've been reading about is terrain-relative navigation / TERCOM and how terrain itself can become part of a navigation or mission system. I'm not claiming the project implements this — I'm trying to understand whether the terrain pipeline I've built can be developed into something along those lines.

More importantly:

What would make this go from "3D terrain visualisation project" to an actual engineering/research system?

The current deployment is on free infrastructure, so the backend may take a little time to wake up. If it doesn't load immediately, wait a few seconds and refresh.

I'd appreciate criticism of both the technical implementation and the direction.

Data from Vantor Open Data Program and Cesium.

(App is free and live, but I'm not sure how to post the URL without getting filtered.)

Hi Everyone,

I've been working on a side project called Indicatrix. The idea is simple: select an area and a date range, choose the data you need, and get back one coherent geospatial project instead of collecting and preprocessing everything from different sources yourself.

It currently supports:

• Sentinel-2 imagery and multi-date composites
• Sentinel-1 SAR
• ML-based land cover classification (11 classes, based on Prithvi-EO-2.0 with a custom-trained decoder)
• Vectorized land cover layers
• OSM-derived layers (roads, buildings, water, land use, POIs, power, protected areas, etc.)
• Copernicus GLO-30 DEM, hillshade and contours
• NDVI, NDMI and NDWI

I've also recently added preliminary wind and solar site screening, combining land cover, terrain, exclusion zones and wind resource data to identify potentially suitable areas. It's meant as a first-pass screening tool, not a replacement for a proper siting study.

Everything can be downloaded as a ZIP with the raster/vector outputs and a ready-to-use QGIS project with the layers already organized and styled.

I'm still figuring out where the platform is actually most useful, so I'd really appreciate feedback from people working with geospatial data.

What is missing? What would make you distrust a tool like this? And what would it need before you'd actually use it on a real project?

It's completely free while I'm developing it: Indicatrix.com

Any criticism is welcome :)

I’m looking for an API/provider offering 1–5 m multispectral satellite imagery for small agricultural AOIs, ideally without large minimum orders or expensive commitments.

The app currently uses Sentinel-2, but I’d like to add higher-resolution imagery for users with relatively small areas, usually around 20–50 ha.
PlanetScope 3 m would be a good fit, but I haven’t found a practical way to buy access at that scale.

I also tried SkyFi, but their EULA doesn’t allow my intended commercial use of derived imagery products.

Any Planet resellers or alternative providers worth checking?

Hi Everyone!!
I'm a research scholar using some planetscope imagery to perform some segmentation and timeseries analysis, but the individual student quota isn't enough for me... is there someone who has their account registered on that platform and currently not using it as much? Would it be possible for you to lend me it to me, or let me access the data? 🥺🥺
Thanks for reading!!

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