5 papers
MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
M. L. Carroll, J. Li, S. D. Guzewich +3
We investigate the transferability of Earth weather foundation models to planetary atmospheres by adapting the GraphCast graph neural weather forecasting model to Mars. While Graph…
SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models
Xingyan Li, Jordan A. Caraballo-Vega, Jie Gong +2
Geospatial foundation models (GeoFMs), pretrained on large-scale geospatial data such as Earth observation (EO), climate, and weather data, have shown promising performance when fi…
Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values
Brian P. Powell, Jordan A. Caraballo-Vega, Mark L. Carroll +4
We report the discovery that binary encoding allows neural networks to extrapolate periodic functions beyond their training bounds. We introduce Normalized Base-2 Encoding (NB2E) a…
Mapping bathymetry of inland water bodies on the North Slope of Alaska with Landsat using Random Forest
Mark L. Carroll, Margaret R. Wooten, Claire E. Simpson +6
The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the wat…
SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery
Caleb S. Spradlin, Jordan A. Caraballo-Vega, Jian Li +3
Foundation models have the potential to transform the landscape of remote sensing (RS) data analysis by enabling large computer vision models to be pre-trained on vast amounts of r…