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.

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.)

Github Details

If you're looking for a way to get into Python, please check out these courses provided by the University of Helsinki for free! I've loved them and had no coding knowledge prior to these courses.

https://geo-python-site.readthedocs.io/en/latest/index.html

https://autogis-site.readthedocs.io/en/latest/index.html

Still finishing the second course, but it has helped me immensely.

http://omnisci.link/t99hjj

You draw a rectangle on the map, pick an analysis type (NDVI, fire risk, moisture, forest health, etc.), and it pulls real Sentinel-2 satellite imagery from ESA, computes the spectral indices, and gives you results with an AI-powered interpretation. https://geotown.io/

What it does:

• + analysis types — vegetation density, drought severity, fire risk, deforestation, water stress, burn scars

• AI interpretation that explains what the numbers actually mean

• Export to PDF reports, CSV, GeoTIFF

• Set up monitoring on any area and get email alerts when conditions change

Some things I found while testing:

• You can clearly see irrigation patterns in farmland vs. rainfed areas using moisture analysis

• Urban heat islands show up dramatically when comparing vegetation in city centers vs. parks

• Deforestation detection actually works — you can see logging roads appearing between two dates

I'd love for people to try it on areas they know well and tell me if the results make sense. What's your neighborhood's vegetation health score?

https://geotown.io/

• Got 600 curious enough users to try our v0.1 (thanks to opensource)

• Translated the plugin in 4 languages to make it REALLY accessible

• Pushed 3 new versions (enhance UI, userflow, precision, control)

• A crazy good tutorial in spanish about our plugin from Hennessy Amor (before we even have time to do our own tutorial with Lilien lol)

• dozen of calls/meetup scheduled with GIS professional, so we can make the plugin useful in their geospatial workflow, we want to help, not just make cool AI demo honestly

It's been so awesome & really motivating to see that the QGIS community are actually receptive of new techs/tools

Only the beginning for us hehe, there are so much to do with AI in Geospatial, open source is great !

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