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#GeoAI

1 post1 participant0 posts today
Public
Adam Steer

Australian geospatial thought leadership™️ has a massive boner for geoAI right now - as in the JMSU, 1m resolution magicked from 10m data variety. Not the mislabeled statistical model variety.

...and a sane "people, have you not watched this approach fuck up every single thing so far?" approach is just not tolerable.

Better go make a picture of myself as an action figure. Maybe thats how to get hired?

🤷

#jmsuAI#geoAI
Public
Anita Graser 🇪🇺🇺🇦🇬🇪

TIL about a new European data-driven weather forecasting model, the Artificial Intelligence/Integrated Forecasting System (AIFS) that outperforms leading physics-based global numerical weather prediction
ecmwf.int/en/newsletter/178/ne

HT linkedin.com/posts/shoaibburq_

ECMWFAIFS: a new ECMWF forecasting system 
#GeoAI#weatherforecast
Public
Spatialists

GeoAI: State-of-play in 2025: The Innovation Booster #ArtificialIntelligence has released a concise review of #GeoAI in Switzerland. The report highlights significant players, innovations, and developments in the field, showcasing how #AI is being integrated into #GIS...
spatialists.ch/posts/2025/03-2 #GIS #GISchat #geospatial #SwissGIS

spatialists.ch – geospatial newsGeoAI: State-of-play in 2025 – spatialists.ch – geospatial news
More from Spatialists
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HeiGIT

Paper Alert ❗
Can we use AI and social media posts to predict pandemic outbreaks?

A study by David Hanny et al. presents a new GeoAI framework that integrates mobile phone data and geo-referenced social media posts to assess their relationship with COVID-19 incidence rates in Rio de Janeiro.

The findings show that social media activity related to COVID-19, tourism and leisure activities strongly correlates with infection rates.

📄 Read more: heigit.org/new-paper-an-explai

Aerial view of Rio de Janeiro
#GeoAI#PublicHealth
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HeiGIT

AI tools and open geodata can help humanitarian practitioners in taking informed decisions, optimizing operations, and fostering community resilience with disaster risk reduction and anticipatory action.

We are looking forward to discussing emerging technologies at the Humanitarian Networks and Partnership Weeks 2025:

💡 Session "GeoAI in Action: Practical Tools for Humanitarian Impact"
🗣️ Sukanya Randhawa
📅 24 Mar 25, 11:00-12:30
📍 Geneve 🇨🇭 & online

ℹ️ heigit.org/events/geoai-in-act

#HNPW#AI#GeoAI
Public
HeiGIT

We have released #SketchMapTool v2.1!
This version uses a larger training dataset and an updated machine learning model for sketch detection to deliver a higher accuracy in digitizing hand-sketched markings.

ℹ️ Sketch Map Tool is an open-source web app for participatory mapping that enables the creation, digitization, and analysis of paper maps.

📄Read about SMT v2.1 and access the training dataset & ML model: heigit.org/digitizing-maps-wit

✏️Try it out: sketch-map-tool.heigit.org/

#geoAI#OpenDataDay
Public
HeiGIT

This new paper by Steffen Knoblauch et al. explores how students are exposed to crime while commuting to schools in Rio de Janeiro 🇧🇷.
Relying on GeoAI techniques, the study reveals inequalities in geographic access to education -- dispute areas increase travel time to the nearest school by 49%.

📋 A concise overview of the study: heigit.org/new-paper-crime-ass
📄 Full paper: sciencedirect.com/science/arti

#geoAI#crime#RioDeJaneiro
Public
Anita Graser 🇪🇺🇺🇦🇬🇪

I really enjoyed the interdisciplinary collaboration sparked by the gAia project on predicting #landslides, resulting in our recent #preprint:

📝 "Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping"

nhess.copernicus.org/preprints

nhess.copernicus.orgBrief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mappingAbstract. Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modeling process. This is particularly important in applied research, where output is used and interpreted by scientists and decision makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital for spatial planning and risk assessment. We present a novel type of landslide susceptibility map that jointly visualizes the estimated susceptibility and the corresponding prediction uncertainty, using an example from a mountainous region in Carinthia, Austria. We also provide implementation guidelines to create such maps using popular free and open-source software packages.
#QGIS#RStats#GIScience
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