#geoAI #gis researchers in my feed, I failed to see how this https://research.google/blog/mapping-the-modern-world-how-s2vec-learns-the-language-of-our-cities/ is "a significant step toward foundational intelligence for #Geography "

Rasterizing data (how did they handle support change?) and reducing dimensionality is nothing new (is innovation here that llm are good at "analyzing" some kind of "eigenvalues"?).

Also is "embedding" overused?

what I am missing?

Mapping the modern world: How S2Vec learns the language of our cities

The Call for Abstracts for the Open-Earth-Monitor Global Workshop 2026 is closing soon ‼️Don’t miss your chance to be part of the final event of the project.

Join us 7–9 October 2026 in Barcelona, Spain, to share your work on #EarthObservation, #GeoAI, and #openData across key themes:
🌿 Forests & Biodiversity
🌾 Soil, Water & Agriculture
🌡 Climate & Health

🔗 Submit your abstract: https://pretalx.earthmonitor.org/global-workshop-2026/cfp
🌐 Learn more: https://earthmonitor.org/global-workshop-2026/

New 📚 Release! GeoAI with Python: A Practical Guide to Open-Source Geospatial AI by Qiusheng Wu

Find it on Leanpub!

Link: https://leanpub.com/geoai

#books #newrelease #python #geoai #ai #satellite #technology #opensource

New 📚 Release! GeoAI with Python: A Practical Guide to Open-Source Geospatial AI by Qiusheng Wu

Satellites capture massive volumes of imagery every day, but turning pixels into insight requires AI. This book teaches you to build, train, and apply deep learning models to real satellite imagery using Python and open-source tools, with 23 chapters of executable code you can run today.

Find it on Leanpub!

Link: https://leanpub.com/geoai

#books #newrelease #python #geoai #ai #satellite #technolog

GeoAI with Python: A Practical Guide to Open-Source Geospatial AI by Qiusheng Wu is the featured book on Leanpub!

Satellites capture massive volumes of imagery every day, but turning pixels into insight requires AI. This book teaches you to build, train, and apply deep learning models to real satellite imagery using Python and open-source tools, with 23 chapters of executable code you can run today.

Link: https://leanpub.com/geoai

#Python #books #newrelease #geoai #technology #science

GeoAI with Python

Hands-on guide to applying deep learning to satellite imagery with Python. Covers segmentation, object detection, foundation models, and QGIS plugins using open-source tools.

What role does the human-in-the-loop play in AI-assisted mapping?

In a recent experiment with YouthMappers in Ghana, we explored how editors interact with AI-generated roads in #OpenStreetMap. Do human edits refine the data or simply pass it through?

Understanding these actions helps improve trust, validation, and quality. It also guides how the community works with growing AI-generated data. What is your take on AI-mapping?

🔗 https://heigit.org/ai-assisted-mapping-insights-from-the-community/

#AIMapping #GeoAI #VGI #GIS

Review of the AI Segmentation by TerraLab plugin for QGIS

https://videos.qwast-gis.com/w/cw6ATTq1PeKEBAazkH1XHU

Review of the AI Segmentation by TerraLab plugin for QGIS

PeerTube
Review of the QGIS GeoAI plugin

PeerTube
New research highlights how AI-driven models are reshaping spatial analysis and land-use planning—integrating large-scale data, predictive modeling, and decision support into geographic workflows.
But the real shift is not just better analysis: planning itself becomes a data-driven, continuously updated process, where models increasingly co-produce spatial decisions.
#AI #GeoAI #UrbanPlanning
https://www.mdpi.com/2073-445X/15/3/460
https://doi.org/10.3390/land15030460
(Open) Webinar - Introduction to ML/DL Theory - Explore the foundational theory behind Machine Learning (ML) and Deep Learning (DL) in a geospatial context
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https://events.teams.microsoft.com/event/f6f7b736-1fa2-4934-aee9-895685232fb4@05c95b33-90ca-49d5-b644-288b930b912b <-- shared webina registration
--
H/T Eric Loubier | DG, Canada Centre for Mapping and Earth Observation
“🌎 GeoAI Webinar Series
March 5, 2026 (11:00 am–3:00 pm ET)
Module #1: Introduction to ML/DL Theory
• Explore the foundational theory behind Machine Learning (ML) and Deep Learning (DL) in a geospatial context…”
#webinar #open #free #GeoAI #UNGGIM #ArtificialIntelligence #Geospatial #Webinar #Canada #Americas #eLearning #onlinelearning #AI #NorthAmerica #Algorithms #Architectures #ML #DL #GIS #spatial #mapping #machinelearning #deeplearning #usecase #workflow #spatialdata #remotesensing