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Linus Mathias Scheibenreif
Last Name
Scheibenreif
First name
Linus Mathias
Email
linus.scheibenreif@unisg.ch
Phone
+41 71 224 26 98
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1 - 2 of 2
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PublicationA Novel Dataset and Benchmark for Surface NO2 Prediction from Remote Sensing Data Including COVID Lockdown MeasuresNO2 is an atmospheric trace gas that contributes to global warming as a precursor of greenhouse gases and has adverse effects on human health. Surface NO2 concentrations are commonly measured through strictly localized networks of air quality stations on the ground. This work presents a novel dataset of surface NO2 measurements aligned with atmospheric column densities from Sentinel-5P, as well as geographic and meteorological variables and lockdown information. The dataset provides access to data from a variety of sources through a common format and will foster data-driven research into the causes and effects of NO2 pollution. We showcase the value of the new dataset on the task of surface NO2 estimation with gradient boosting. The resulting models enable daily estimates and confident identification of EU NO2 exposure limit breaches. Additionally, we investigate the influence of COVID-19 lockdowns on air quality in Europe and find a significant decrease in NO2 levels.Type: conference paper
Scopus© Citations 4 -
PublicationBen-ge: Extending BigEarthNet with Geographical and Environmental Data( 2023-07-04T14:17:54Z)
;Michael Mommert ;Damian BorthBegum DemirDeep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in most cases, only single or few modalities are typically considered. In this work, we present the ben-ge dataset, which supplements the BigEarthNet-MM dataset by compiling freely and globally available geographical and environmental data. Based on this dataset, we showcase the value of combining different data modalities for the downstream tasks of patch-based land-use/land-cover classification and land-use/land-cover segmentation. ben-ge is freely available and expected to serve as a test bed for fully supervised and self-supervised Earth observation applications.Type: conference contribution