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Found 9 papers, 3 papers with code
High-Resolution Building and Road Detection from Sentinel-2
Mapping buildings and roads automatically with remote sensing typically requires high-resolution imagery, which is expensive to obtain and often sparsely available.
A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts
Post-processing typically takes the outputs of a Numerical Weather Prediction (NWP) model and applies linear statistical techniques to produce improve localized forecasts, by including additional observations, or determining systematic errors at a finer scale.
Global Extreme Heat Forecasting Using Neural Weather Models
We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss.
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Machine Learning for Precipitation Nowcasting from Radar Images
High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather.
Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices.
Data-driven discretization: a method for systematic coarse graining of partial differential equations
Many problems in theoretical physics are centered on representing the behavior of a physical theory at long wave lengths and slow frequencies by integrating out degrees of freedom which change rapidly in time and space.
Disordered Systems and Neural Networks Computational Physics
