21 citations · 21 across the 5 of their papers we have counts for
5 papers · 1 filter
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
Wenwen Li, Sizhe Wang, Hyunho Lee +4
Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventi…
Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves
Aman Gupta, Aditi Sheshadri, Sujit Roy +5
Global climate models parameterize a range of atmospheric-oceanic processes like gravity waves, clouds, moist convection, and turbulence that cannot be sufficiently resolved. These…
Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal +30
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observator…
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
Sujit Roy, Dinesha V. Hegde, Johannes Schmude +22
This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine…
Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery
Philip Wootaek Shin, Vishal Gaur, Rahul Ramachandran +4
High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downst…