In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery.
Jacquelyn SheltonPrzemyslaw PolewskiWei YaoPublished in: CoRR (2021)
Keyphrases
- high resolution
- satellite imagery
- high risk
- remote sensing
- satellite images
- quantile regression
- change detection
- multispectral
- remote sensing images
- cross validated
- least squares
- land cover
- risk factors
- prostate cancer
- urban areas
- cross validation
- response variable
- neural network
- log likelihood
- decision trees
- data mining