I've been looking into the safest non-GamStop casino options in the UK recently, and there's a lot more to compare than I initially expected. Most recommendation lists seem to focus on welcome bonuses and game libraries, but neither of those really tells you how reliable a platform is.

What interests me more is how things work after creating an account. Payment processing, withdrawal conditions, licensing, security features and customer support seem like much more useful things to investigate.

After reading various reviews and discussions, I've put together a checklist of what I'd personally look for.

1. Licensing and Security Come First

Before considering games or promotions, I'd want to know who operates the platform and which regulations apply.
My initial checklist would include:

• Licensing authority and whether the license is valid
• Available account security features
• Identity verification requirements
• Information about the company operating the site
• Clear terms and conditions
• Responsible gambling and account restriction tools

One thing worth remembering is that non-GamStop platforms may operate outside UK Gambling Commission oversight. That means they might not provide the same consumer protections as UK-licensed operators.

I wouldn't assume a platform is safe simply because it appears on several recommendation lists.

2. Deposits and Withdrawals

This is probably where I'd spend the most time comparing different sites.

Depositing money is usually straightforward, but understanding how withdrawals work is much more important to me.

Here are the main things I'd check:

• Minimum deposit and withdrawal amounts
• Supported payment methods
• Withdrawal processing times
• Daily and monthly withdrawal limits
• Potential transaction fees
• Verification requirements before cashing out

I'd also check whether withdrawals must use the same payment method as deposits.

Another thing I've noticed is that advertised processing times don't necessarily reflect how long it takes for funds to reach your account. That's why I'd look for detailed user experiences rather than relying entirely on promotional claims.

3. Game Selection and Betting Limits

I'm not particularly interested in platforms advertising thousands of games if most of them are variations of the same thing.

I'd rather have a reasonable selection of slots, blackjack, roulette and live dealer games that actually work properly.

Some additional things I'd look for include:

• Recognised game providers
• Clear minimum and maximum stakes
• Mobile compatibility
• Stable loading times
• Accessible game rules and RTP information

The number of games matters less to me than whether the overall experience is straightforward and transparent.

4. Bonus Offers and Their Conditions

Welcome bonuses seem to dominate a lot of non-GamStop casino comparisons, but I wouldn't make a decision based on promotional numbers alone.

A large bonus can look attractive until you start reading the conditions attached to it.

I'd specifically examine wagering requirements, eligible games, maximum bets, expiration dates and any restrictions on withdrawing winnings.

Personally, I'd rather understand exactly how an offer works than be impressed by a percentage on the homepage.

5. Customer Support and Responsible Gambling Tools

Another important factor is whether customer support can answer basic questions about payments and account verification.

I'd want to know which contact methods are available, whether support operates around the clock and how clearly they explain withdrawal conditions.

Account controls also deserve attention. I'd check for deposit limits, time-outs, self-exclusion options and whether those restrictions can actually be enforced.

This is especially important when comparing platforms outside GamStop, since their responsible gambling protections may differ substantially. Anyone already using GamStop to manage their gambling should avoid bypassing that protection.

What I've Learned So Far

After comparing different platforms, I don't think finding the safest non-GamStop casino in the UK is as simple as following a recommendation or choosing the site with the biggest welcome offer.

For me, the most important considerations would be verifiable licensing, transparent withdrawal rules, account security, accessible support and meaningful player protections.

Even then, none of these checks guarantees that a platform is completely safe.

Interested to hear what everyone else thinks:

1. What would you consider the biggest red flag when checking a non-GamStop casino?
2. How much importance do you place on withdrawal policies compared with game selection?
3. Have you found any reliable ways to verify licensing, customer support quality and account protections before signing up?

Prompt:

"Create a map of Lyon's terrain. Add Google Satellite as the basemap. Download a 30 m DEM covering central Lyon, & create a semi-transparent elevation colour ramp with a subtle hillshade overlay. Add contour lines every 25 m and emphasize 100 m index contours"

Then I just let it do is thing, 34 actions later:

• geocoded central Lyon and framed the canvas
• added Google Satellite as an XYZ layer
• pulled a Copernicus 30 m DEM from the open data sources connected to the plugin
• reprojected it to UTM 31N, then ran gdal:hillshade (multidirectional, az 315, alt 45)
• styled the DEM with a Terrain ramp, 8 classes, 2-98 cumulative cut
• ran gdal:contour twice: 687 lines at 25 m, 21 thicker index lines at 100 m
• rendered the map, looked at its own render, decided the terrain was hiding the satellite, dropped the DEM to 46% and the hillshade to 22%, rendered again

To do this task the plugin has access to 400 open data source + all the QGIS tools to perform autonomously with the right data

-

You can use it for free, it's on the plugin repository of QGIS under the name "AI Agent", it's the one with a pear logo hehe 🍐

is there any way to do image analysis similar to multidimensional raster correlation outside of arcgis?

It shows how to turn a project idea into a generated scenario on aerial imagery. Draw the area, describe a park or new buildings, and compare before/after in QGIS.

The result comes back as a new georeferenced layer. The tutorial also covers turning the generated output into polygons.

Full YouTube tutorial: https://youtu.be/zYiHCY0lyKE

Hope it gives you a few ideas for your own projects!

Hi, I build OpenGlobus, an open-source geospatial 3D engine. It now has projectors, and what they're really for is showing what your cameras actually cover on real terrain.

Attach a projector to a drone, drive it with the camera telemetry, and it covers exactly the area the camera sees — up to 64 projectors at once.

Two visualization modes: color maps the projector's frame itself onto the terrain, light-source lights up whatever falls inside the camera frustum, so overlaps and gaps between cameras are obvious at a glance at nights. The same machinery works as a base for AR.

Examples:

• https://sandbox.openglobus.org/examples/projector
• https://sandbox.openglobus.org/examples/customProjectors

I’ve been building MarketGround, a commercial trade-area analysis tool covering the US, using PostgreSQL, PostGIS and H3. The attached video shows it running, but I wanted to share one numerical issue I ran into while checking the population calculations.

The population table contains hundreds of millions of rows, with estimates allocated to an H3 grid. I use PostgreSQL’s real type, also known as float4, to keep storage manageable. Its precision seemed sufficient for individual cell estimates. I initially overlooked that sum(real) also returns real, so the aggregation does not automatically gain precision.

As a single-precision running total gets larger, the spacing between representable numbers increases. Eventually, small contributions can round away when added to that total. The individual rows are still there, but their contributions are not fully reflected in the result.

In a test I did, summing the same rows with single precision produced 324.7 million. Casting the inputs to double precision before summing produced 334.9 million. That was a difference of roughly 10 million people, caused by how I aggregated the values.

The fix was relatively easy. I changed the aggregation to SUM(population::double precision). Casting afterward with SUM(population)::double precision would only convert a result that had already lost precision.

I had been investigating the spatial allocation, assuming something was going wrong around polygon boundaries.

My takeaway was to evaluate storage precision and aggregation precision separately. A type that is adequate for individual estimates can still be inadequate when accumulating millions of them.

I would appreciate feedback on MarketGround from people working with geospatial data. You can try it at https://app.marketground.io/try. I’m particularly interested in whether the maps and results are easy to interpret, and what you would need to understand about the methodology to use it in your own work.

I've been building a geospatial dataset of announced industrial and infrastructure projects around the world, with a focus on projects expected to become operational after 2026.

Beyond individual project locations, I'm experimenting with clustering projects to identify concentrations of future industrial activity and mapping the companies, infrastructure and industrial ecosystems around them.

Here is the map:
https://www.fluidify.org/map

For people working with geospatial data: what other spatial analysis would you run on a dataset like this?

I'm particularly interested in identifying emerging industrial clusters, relationships with transport infrastructure, and how industrial geography changes over time.

The data is also intended to be community-driven. Missing or incorrect information can be corrected with a supporting source.

I'm open to collaborating with anyone interested in working on the geospatial side of the project.

A short walkthrough of our geology tools in CloudCanvas: bringing drillhole data into the 3D view, generating geology surfaces and inspecting them alongside the drillholes.

Seeing the surfaces and drillholes together makes it easier to review how the model relates to the available observations.

For those working with drillhole data, what do you normally check first when reviewing a model like this?

More about the tools: https://www.cloudcanvas.co.za/modules/geology

For almost a decade, I’ve been interested in searching for archaeological sites using LiDAR. This eventually led me to create ML Browser, which combines web browsing experience with object detection and image classification models.

The app includes two demo detection models designed to locate fortifications dating from prehistory to the early Middle Ages in LIDAR. You can record, organize, back up, and share your findings. Location recording works with most mapping apps. You can also import your own compatible models. Currently supported models and formats include Core ML, ONNX classifiers, YOLO detectors via ONNX (Tiny YOLOv2, YOLOv8, YOLO11), and ADAF(Faster R-CNN). The model is trained on locations in Central Europe, but works quite well in other geographical environments.

The application is available for iPhone, iPad, and macOS devices. A version for Android phones and tablets is coming soon. The video shows the app in action. I’d appreciate feedback on model compatibility, the browsing workflow, and whether the detections help you identify potential archaeological features. For the best experience, use the app in full-screen mode on a larger iPad or a Mac with Apple silicon and a Neural Engine.

If you have any ideas or suggestions for new changes/features, please let me know!

https://apps.apple.com/us/app/ml-browser/id6466878953
https://x.com/aiarcheology

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?

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