Estimation of Air Pollution with Remote Sensing Data

Item Type Conference or Workshop Item (Poster)
Abstract Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fos- sil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mit- igate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Exist- ing models for surface-level air pollution rely on extensive land-use datasets which are often lo- cally restricted and temporally static. This work proposes a deep learning approach for the pre- diction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satel- lite imagery with satellite-based atmospheric col- umn density air pollution measurements enables the scaling of air pollution estimates (in this case NO2) to high spatial resolution (up to ∼10m) at arbitrary locations and adds a temporal compo- nent to these estimates. The proposed model per- forms with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error <6 μg/m3). Our results en- able the identification and temporal monitoring of major sources of air pollution and GHGs.
Authors Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian
Research Team AIML Lab
Language English
Subjects computer science
HSG Profile Area None
Date 1 May 2022
Event Title Swiss Remote Sensing Days
Event Location Ascona
Event Dates 1-4 May 2022
Depositing User Linus Mathias Scheibenreif
Date Deposited 06 Dec 2022 09:00
Last Modified 28 Feb 2023 12:41


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Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian: Estimation of Air Pollution with Remote Sensing Data. [Conference or Workshop Item]

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