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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Authors: Scheibenreif, Linus; Mommert, Michael; Borth, Damian;

Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Abstract

Description This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements. The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly). The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading. Content The data is organized into the following files: README.md - this file sentinel-2-eea.tar.gz [33.1GB] sentinel-5p-eea.tar.gz [80.1GB] samples_2018_2020_eea.csv samples_quarterly_eea.csv samples_monthly_eea.csv sentinel-2-epa.tar.gz [0.15GB] sentinel-5p-epa.tar.gz [1.8GB] samples_2018_2020_epa.csv samples_quarterly_epa.csv samples_monthly_epa.csv Acknowledgement If you use this data set, please cite our publication: Scheibenreif, L., Mommert, M., Borth, D., "Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space", Tackling Climate Change with Machine Learning workshop at ICML 2021. Please refer to this publication for additional information on the data set. This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA. Responsible Author Linus Scheibenreif University of St. Gallen, Institute of Computer Science Chair Artificial Intelligence and Machine Learning linus.scheibenreif ( at ) unisg.ch

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Keywords

remote sensing, machine learning, nitrogen dioxide, air pollution, computer vision

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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