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Continuous and reliable Earth observation is critical for real-world systems like hurricane forecasting and wildfire detection. GAIA demonstrates promising capabilities, both in filling large gaps in satellite records and converting satellite imagery into precipitation estimates. GAIA represents a new class of geospatial AI models with the potential to create significant impact across sectors—from more accurate weather forecasting to applications in insurance, power utilities, aviation, and agriculture.
Source identity and required public metadata were editorially reviewed on 2026-09-15.
Author unknown. Date unknown. GAIA Foundation Model. https://huggingface.co/bcg-usra-nasa-gaia (AI & Environment Resource Hub; record atlas-47bf2a6f4ffb; collection snapshot 2026-09-15).
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Kalai Ramea, founder of Planette AI, describes her company’s focus on the “forecast gap,” the critical two week to two month window between short term weather and long term climate models. Drawing on her background in climate policy and research at Xerox PARC, she explains that Planette AI’s “scientific AI” learns directly from physics based earth system simulations rather than relying on historical weather data, making forecasts more reliable in a changing climate. This approach allows for faster, more actionable insights that can guide real world decisions for industries like agriculture, aviation, insurance, and event planning, including detecting the kind of heavy rain signals that could have prevented the last minute cancellation of events like Bonnaroo.
Apple Podcasts
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
A new family of spatial-extent coefficients assesses extreme georeferenced events by measuring spatial spread from threshold exceedances, with a semiparametric model enabling statistical extrapolation, demonstrated through simulations and gridded temperature data in France.
Extreme Events, Machine Learning
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
Bellwether, an AI-powered climate prediction tool, analyzes Earth observation data to aid disaster preparedness and recovery, with Dr. Sarah Russell leading its efforts at the intersection of business, planetary health, and machine learning.
Apple Podcasts
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
A new #GeoAI tutorial series where I'll guide you through using the powerful GeoAI Python package for integrating AI with geospatial data analysis and visualization.
