Thank you to Development Seed, one of the very first OSM US organizational members! Our team is grateful for Dev Seed's sustaining support and engagement!

U.S. Geological Survey [#USGS] Geomagnetic Variometer Data - Capitalizing On Seismic Infrastructure
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https://doi.org/10.1785/0220250185 <-- shared paper
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#geomagnetic #geomagnetism #USGS #USA #CONUS #seismology #seismograph #measurement #spatialanalysis #spatial #stations #SeismographicNetwork #magneticfield #readings #spatiotemporal #magnetometer #collabration #spaceweather #risk #hazard #colocation #magneticstorm #earthquake #interdisciplinary #research #sampling #multidisciplinary #fielddata #mapping #GIS #cohosted #corrleation #sampling
@USGS

Im nächsten @foss_academy Web-Seminar im Dezember geht es um

Einführung in QGIS Server

Donnerstag 4. Dezember 2025

Kostenloses einstündiges Web-Seminar
10.00 - 11.00 Uhr
Jetzt schon anmelden!
https://foss-academy.com/web-seminare/
#FOSSGIS #GIS #GEOSPATIAL #FOSS4G #OSGeo #GEOSPATIAL #OSM

In GeoIntelligence, pricing shouldn’t be a guessing game. Too often, organizations face hidden fees, unpredictable renewals, and license limits that slow growth.

At Quarticle, we took a different path: transparent, scalable, and free from vendor lock-in. Our flat annual pricing, Kubernetes-powered infrastructure, and open standards give teams clarity, flexibility, and long-term value.

Read the full story: https://quarticle.ch/blog/predictable-costs-scalable-intelligence-why-transparent-pricing-matters-in-geointelligence

Presenting: UN MAPS CLINICS 🗺

The United Nations Global Service Centre (UNGSC), in collaboration with UN OICT and the UN Geospatial Network, is pleased to announce the #UNMaps Clinics!

DETAILS:
Theme: “Enhancing UN Maps – from the field, for the field.”
🗓 1-4 December 2025
Online
Programme: https://maps.un.org/conference/2025

Interested? Please contact us via maps[at]un.org.

Stay tuned for updates!

GDAL Released: v3.12.0 https://geoobserver.de/2025/11/10/gdal-released-v3-12-0/ via @GdalOrg @EvenRouault #gdal #ogr #qgis #switch2qgis #fossgis #foss4g #opensource #gistribe #gischat #spatial #geospatial #gis #geo #geoObserver pls RT

#30DayMapChallenge – Day 9: Analog European QGIS User Groups on a natural canvas of autumn leaves. #GIS #Cartography

#30DayMapChallenge – Day 9: Analog
It was a beautiful autumn day here in the Netherlands, and I decided to bring a bit of that seasonal magic into my mapmaking. I collected autumn leaves and used them as the background for today’s map.

On top of this natural canvas, I highlighted in orange the European countries that have official #QGIS User Groups. Data from the QGIS Easter Egg.

Random Forests and Living Trees

English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.

What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.

The methodology developed during that work is still being used today — adapted for new environmental and urban projects.

https://www.datastory.org.ua/random-forests-and-living-trees/

The Blueberry Map Experiment — modelling meets the mountains

In 2022, while living with my family in the Czech Republic, I built a digital map of wild blueberry hotspots in the Jizera Mountains.

At first, it looked like a fun summer project — our neighbors used the map to find the best berry spots and enjoy the landscape.
But behind it was a serious experiment: I tested species distribution modelling (SDM) methods, later adapted for wide-world rare earth element prediction.

Within this “blueberry project” I:
automated the full spatial workflow in R and QGIS,
generated geomorphons and other terrain-based predictors,
built and validated ML models,
created probability maps and tested them in the field.

What started as a family hobby became a field-tested workflow for predictive geoscience.

#30DayMapChallenge Day3 Polygons – worldwide Google search interest in cat vs dog. Apparently I got a thing for cats now

Map shows if search volumes are higher for cat (blue), dog (red) or same (grey). Light colours mean low search volumes. Data from Google Trends, Oct 2024-Oct 2025.

Made in #python + #Figma to add text, code https://github.com/Lisa-Ho/30-day-map-challenge/tree/main/2025

Calgary’s vegetation — satellite comparison (2024 → 2025)

Median NDVI maps from mid-May to mid-September show a clear difference between the two seasons.

In 2025, NDVI values are noticeably higher — vegetation stayed greener and denser for longer.
The wetter summer had a strong effect on canopy productivity across most Calgary communities, especially in parkland and tree-covered zones.

Based on Sentinel-2 imagery and R + QGIS processing.

“If the map does not match the terrain — trust the terrain!”
— Principle of field geoscience

This is how the effective catchment area of the Inhulets River looks within the study region.

The upstream part — above the Karachunivske Reservoir, the outlet of the Saksahan derivative tunnel, and the confluence of the Stara Saksahan River — was excluded from the calculation.

Within the analyzed area, surface runoff is possible only from the highlighted zone.
The rest of the “catchment basin” is hydrologically inactive: runoff is intercepted by ponds, settling tanks, and other anthropogenic landforms.

The analysis was based on the Copernicus GLO-30 DEM, integrated with hydrological modeling and terrain processing in open-source GIS.

Calgary Greenness Dynamics (2024–2025)

Mapping ΔNDVI between the summers of 2024 and 2025 shows how Calgary’s communities changed in their vegetation cover.

Some areas became noticeably greener this year — likely due to the wetter and milder summer.
Others show slight declines, possibly linked to construction, soil dryness, or limited tree canopy recovery.

These patterns reveal how different parts of the city respond to seasonal variability — and where future urban greening might have the most impact.

Based on Sentinel-2 data and NDVI analysis in R.

Where Calgary Got Greener — and Where It Didn’t?

A quick look at how Calgary’s residential communities changed in greenness (NDVI) between 2024 and 2025.

Some neighbourhoods show a clear recovery of vegetation — probably thanks to a wetter, milder summer, better soil moisture, or local greening efforts.
Others stayed stagnant or even lost NDVI — maybe new construction, dry soils, or sparse vegetation played a role.

The bar chart shows Top-5 and Bottom-5 communities by NDVI change. It’s fascinating how uneven the “greening pulse” can be within one city.

Based on Sentinel-2 data, mid-May – mid-September, processed in R.

A discussion of geospatial embeddings: Ed Parsons’ latest article cautions that while geospatial #embeddings promise analytical power, they risk transparency and interpretability long valued in #remotesensing. The article underscores the need to balance innovation with auditability and...
https://spatialists.ch/posts/2025/11/08-a-discussion-of-geospatial-embeddings/ #GIS #GISchat #geospatial #SwissGIS

For Day 06 of the #30DayMapChallenge, I used: Wikipedia retrieving tallest building list with `rvest` `tidyterra` to retrieve elevation shades Refined top buildings's labels and lines with `ggrepel` Code: guillaume-noblet.com/30DayMapChal... #rstats #gis #dataviz #ggplot2 #terra

#30DayMapChallenge Day 8: Urban

On #WorldUrbanismDay, I highlight the most paved municipality in the Netherlands: Krimpen aan den IJssel (43.3%), with Kortland-Noord at nearly 53%.

The Imperviousness Map of NL, made by Friedenau Society for Climate Adaptation Services, derived from AHN LiDAR + BGT, was recently released as a 25 cm COG and used in their Neighbourhood Dashboard: https://buurtdashboard-dev.vercel.app/

Happy to have contributed to this project!

Day 8 of #30DayMapChallenge - Urban

I am guessing urban centers by looking at roads and further ways.

I used a network of roads downloaded from OpenStreetMap to divide the surface into blocks. Then, I classified those blocks according to their size.

These sizes are expected to be smaller in urban centers than in rural areas.

Ya esta disponible La Experimental #13:

Herramientas GIS online
App de notas OpenSource
TUI para Proxmox y visualizador multimedia
Descripciones visuales de algoritmos
Servidores mock para testing
Estado de Django y uso de uv en Docker
Plantillas para ClaudeCode
Music player para GNULinux
Gestión de proyectos SelfHosted con Kanban

Link: laexperimental.substack.com/p/le-13