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Found 14 papers, 2 papers with code
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
Wildfire risk forecast: An optimizable fire danger index
This approach has two benefits: (1) the NFDRS IC parameters can be improved for each region using actual observed fire events, and (2) the internal variables remain intact for interpretations by specialists instead of meaningless hidden layers as in traditional neural networks.
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property.
Controlling Weather Field Synthesis Using Variational Autoencoders
One of the consequences of climate change is anobserved increase in the frequency of extreme cli-mate events.
A comparative study of stochastic and deep generative models for multisite precipitation synthesis
Future climate change scenarios are usually hypothesized using simulations from weather generators.
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network
An impact of climate change is the increase in frequency and intensity of extreme precipitation events.
Decadal Forecasts with ResDMD: a Residual DMD Neural Network
Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society.
Geostatistical Learning: Challenges and Opportunities
regionalized) variables due to spatial correlation; and trends caused by geophysical processes lead to covariate shifts between the domain where the model was trained and the domain where it will be applied, which in turn harm the use of classical learning methodologies that rely on random samples of the data.
A modular framework for extreme weather generation
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.
Learning Multi-Party Turn-Taking Models from Dialogue Logs
This paper investigates the application of machine learning (ML) techniques to enable intelligent systems to learn multi-party turn-taking models from dialogue logs.
Semantic Segmentation of Seismic Images
We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.
Bus Travel Time Predictions Using Additive Models
Many factors can affect the predictability of public bus services such as traffic, weather and local events.
