17 citations · 17 across the 4 of their papers we have counts for
5 papers
PARDIG: Global Precipitation Estimates Learned from Sparse Satellite Observations
Guy Shalev, Stephan Rasp, Boris Babenko +14
Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Exist…
WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
Stephan Rasp, Boris Babenko, Dominic Masters +22
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spati…
Oya: Deep Learning for Accurate Global Precipitation Estimation
Emmanuel Asiedu Brempong, Mohammed Alewi Hassen, MohamedElfatih MohamedKhair +7
Accurate precipitation estimation is critical for hydrological applications, especially in the Global South where ground-based observation networks are sparse and forecasting skill…
An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting
Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong +16
Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly…
High-Resolution Building and Road Detection from Sentinel-2
Wojciech Sirko, Emmanuel Asiedu Brempong, Juliana T. C. Marcos +9
Mapping buildings and roads automatically with remote sensing typically requires high-resolution imagery, which is expensive to obtain and often sparsely available. In this work we…