| 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 |
| URI: |
https://www.alexandria.unisg.ch/publications/268264 |
Estimation of Air Pollution with Remote Sensing Data
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
