Python scripts to download and preprocess air pollution concentration level data aquired from the Sentinel-5P mission
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Updated
Jul 7, 2021 - Python
Python scripts to download and preprocess air pollution concentration level data aquired from the Sentinel-5P mission
Download and process satellite imagery in JavaScript or TypeScript using Sentinel Hub services.
Sentinel5P_Python
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space, presented at Tackling Climate Change with Machine Learning workshop at ICML 2021.
Code repository for "Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing", IEEE TGSRS, 2022
Exploring the effect of COVID-19 in air pollution by using satellite data, with the sentinelsat and cartopy libraries.
Python scripts and Jupyter Notebooks to download and preprocess Sentinel-5P NO2 data.
Automatic pipeline to download, process, and visualize Sentinel-5P data for pollution monitoring
Example of preliminary analysis and data exploration of Sentinel-5P satellite data
Sentinel-5P: Processing and handling NO2 measured values in R
Sentinel-5P Single-Image Super-Resolution Toolbox
Another Sentinel-5 Precursor data query and downloader tool with automation for daily downloads and a ReactJS app that can visualise the data on a map.
Impacts of COVID-19 lockdown on Air quality.
Python scripts used to download and process the Sentinel 5P data
This repository presents a method for optimizing the allocation of air quality monitoring stations using Sentinel-5P images.
A quick list of access points for global air quality data.
Contains python notebooks used for clipping of satellite data using Geojson AOI, calculating LST from Landsat 8 Images and Correlation analysis between CO and LST
Workflow to identify and visualize collocated EnMAP and temporally closest Sentinel-5P TROPOMI acquisitions
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