Options
Michael Mommert
Title
Prof. Dr.
Last Name
Mommert
First name
Michael
Email
michael.mommert@unisg.ch
Phone
+41 71 224 2615
Homepage
Google Scholar
Now showing
1 - 3 of 3
-
PublicationType: conference poster
-
PublicationQuantifying Traffic with Deep Learning from Earth Observation Data( 2022-10-13)
;Blattner, Moritz ;Eicher, LeonardoType: conference poster -
PublicationEstimation of Air Pollution with Remote Sensing Data( 2022-05-01)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.Type: conference poster