29 citations · 29 across the 6 of their papers we have counts for
7 papers
Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery
Simon Donike, Pritthijit Nath, Cristina Radin +5
High-resolution tropical cyclone surface wind speeds derived from synthetic aperture radar (SAR) observations can support storm structure and intensity modelling, but remain spatia…
A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph
Ruben Cartuyvels, Karim Douch, Gabriele Bertoli +4
Continuous monitoring of water surface elevation across river networks is critical for flood forecasting, water resource management, and understanding the global water cycle. Yet,…
OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations
Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola +3
Despite comprising over 70% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere. Understanding the global ocean requires…
SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi +7
Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for…
Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models
Edward Sandra, Lander Vanroye, Dries Dirckx +3
Classical methods in robot motion planning, such as sampling-based and optimization-based methods, often struggle with scalability towards higher-dimensional state spaces and compl…
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,…
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov +3
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-superv…