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

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.

Objective

We present a deep fully convolutional neural network (FCN) for predicting fire smoke in satellite imagery in near-real time (NRT).

Methods

The FCN identifies fire smoke using output from operational smoke identification methods as training data, leverag

SUBMITTER: Larsen A 

PROVIDER: S-EPMC7796988 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature