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 Ann Shelton; Przemyslaw Piotr Polewski; Wei Yao

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 Ann Shelton
, Przemyslaw Piotr Polewski
, Wei Yao

Department of Land Surveying and Geo-Informatics

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Original language	English
Title of host publication	ICML 2020 Workshop on Healthcare Systems, Population Health, and the Role of Health-Tech
Publication status	Published - Sept 2020

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http://hdl.handle.net/10397/100663

Cite this

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title = "In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery",

author = "Shelton, \{Jacquelyn Ann\} and Polewski, \{Przemyslaw Piotr\} and Wei Yao",

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In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery. / Shelton, Jacquelyn Ann; Polewski, Przemyslaw Piotr; Yao, Wei.
ICML 2020 Workshop on Healthcare Systems, Population Health, and the Role of Health-Tech. 2020.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

AU - Shelton, Jacquelyn Ann

AU - Polewski, Przemyslaw Piotr

AU - Yao, Wei

PY - 2020/9

Y1 - 2020/9

UR - https://arxiv.org/abs/2105.02406

UR - https://www.researchgate.net/publication/351368896_In_the_Danger_Zone_U-Net_Driven_Quantile_Regression_can_Predict_High-risk_SARS-CoV-2_Regions_via_Pollutant_Particulate_Matter_and_Satellite_Imagery

M3 - Conference article published in proceeding or book

BT - ICML 2020 Workshop on Healthcare Systems, Population Health, and the Role of Health-Tech

ER -

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

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