Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.

ABSTRACT: Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM_2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video f

SUBMITTER: Feldman A 

PROVIDER: S-EPMC10785748 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature