A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Journal: Journal of exposure science & environmental epidemiology
PMID: 32719441

Abstract

BACKGROUND: Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are vital. New-generation satellite-based sensors produce high-resolution spectral images, providing real-time information of surface features during wildfire episodes. Because of the vast size of such data, new automated methods for processing information are required.

Authors

  • Alexandra Larsen
    Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
  • Ivan Hanigan
    The University of Sydney, University Centre for Rural Health, School of Public Health, Sydney, NSW, Australia.
  • Brian J Reich
    North Carolina State University, Department of Statistics, Raleigh, North Carolina, USA.
  • Yi Qin
    Oceans and Atmosphere Research, Commonwealth Science and Industrial Research Organisation, Victoria, Australia.
  • Martin Cope
    Oceans and Atmosphere Research, Commonwealth Science and Industrial Research Organisation, Victoria, Australia.
  • Geoffrey Morgan
    The University of Sydney, University Centre for Rural Health, School of Public Health, Sydney, NSW, Australia.
  • Ana G Rappold
    U.S. Environmental Protection Agency, Center for Public Health and Environmental Assessment, Office of Research and Development, Research Triangle Park, NC, USA. Ana.Rappold@epa.gov.

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