AI & Environment Resource HubCotsakis, Ryan; Di Bernardino, Elena; Opitz, Thomas · 2025-01-14
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.
Cotsakis, Ryan; Di Bernardino, Elena; Opitz, Thomas. 2025-01-14. Assessing the Size of Spatial Extreme Events Using Local Coefficients Based on Excursion Sets. https://arxiv.org/abs/2310.09075 (AI & Environment Resource Hub; record paper-085; collection snapshot 2026-09-15).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 89 · Original ID: paper-085.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
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.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
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.
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.
Shared topics: Research tools and geospatial methods; Energy and electricity; Climate and greenhouse gases; Weather, hazards, and adaptation
HASTE is an AI-powered disaster assessment framework that leverages satellite imagery and remote sensing data to provide rapid assessment capabilities for humanitarian response teams.