10 papers
On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Paula Harder, Christian Lessig, Matthew Chantry +2
Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fin…
Causal Climate Emulation with Bayesian Filtering
Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn +6
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally e…
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina, Christina Humer, Alexandre Duval +7
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind a…
Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko, Kristof Van Tricht, Gabriel Tseng +6
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover classification, environmental monitoring,…
RainShift: A Benchmark for Precipitation Downscaling Across Geographies
Paula Harder, Luca Schmidt, Francis Pelletier +5
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolution for local-scale risk-assessments is…
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng, Arthur Ouaknine, Etienne Laliberté +3
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, req…