Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions.

Autor: Yadav N; Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; University Space Research Association (USRA), Mountain View, USA. Electronic address: yadav.ni@northeastern.edu., Sorek-Hamer M; University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA., Von Pohle M; University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA., Asanjan AA; University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA., Sahasrabhojanee A; University Space Research Association (USRA), Mountain View, USA; NASA Ames Research Center, Moffett Field, USA., Suel E; Imperial College London, London, UK., E Arku R; University of Massachusetts, Amherst, USA., Lingenfelter V; Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; University Space Research Association (USRA), Mountain View, USA., Brauer M; University of British Columbia, Vancouver, Canada., Ezzati M; Imperial College London, London, UK., Oza N; NASA Ames Research Center, Moffett Field, USA., Ganguly AR; Sustainability and Data Sciences Laboratory, Northeastern University, Boston, USA; Pacific Northwest National Laboratory (PNNL), Richland, USA; The Institute for Experiential AI, Northeastern University, Boston, USA.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2024 Feb 01; Vol. 342, pp. 122914. Date of Electronic Publication: 2023 Nov 22.
DOI: 10.1016/j.envpol.2023.122914
Abstrakt: Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier Ltd.)
Databáze: MEDLINE
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