Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

note based on the PlanetLabs' terms of use (https://www.planet.com/terms-of-use/) and Weather Underground’s terms of use (https://www.wunderground.com/company/legal), we are not allowed to publish/distribute the images data from PlanetLabs or meteorology data from Weather Underground used in this study. However, we have provided a python script (“Image_Downloader.ipynb”) that can download the current study’s images from PlanetLabs and we have provided links (“Links_to_Download_Meteorology_Data.docx”) to direct readers to the corresponding Weather Underground webpages where they can download the meteorology data used in our study.

This dataset includes the following items: 1) raw 1 h PM2.5 mass concentration measurements of the 35 regulatory AQM stations and the US Embassy station in Beijing from January 1, 2017 to July 20, 2019; 2) raw 1 h PM2.5 mass concentration measurements of the 10 regulatory AQM stations in Shanghai from January 1, 2017 to July 20, 2019; 3) "Links_to_Download_Meteorology_Data.docx" links to direct readers to the corresponding Weather Underground webpages where they can download the meteorology data used in our study; 4) "Image_Downloader.ipynb" python code to download the Beijing and Shanghai stations' images used in our study from PlanetLabs; 5) "PM_Meteorology_Image_process_filter_match.ipynb" python code to process the raw PM2.5 data and the raw meteorology data; to process and filter the raw images data; to match the processed PM2.5 data and meteorology data with the filtered and processed images data; store the matched records for model training and evaluation purposes; 6) "Model_Training_and_Evaluation.ipynb" python code to build, train, and evaluate the CNN-RF models used in our study; 7) "670m_model.hdf5" the CNN model trained on the Beijing training dataset at a spatial resolution of 670 * 670 m; 8) "500m_model.hdf5" the CNN model trained on the Beijing training dataset at a spatial resolution of 500 * 500 m; 9) "200m_model.hdf5" the CNN model trained on the Beijing training dataset at a spatial resolution of 200 * 200 m.

Identifier
DOI https://doi.org/10.17632/n3ywbm3y2t.3
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-g6-u5sn
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:159205
Provenance
Creator Zheng, T
Publisher Data Archiving and Networked Services (DANS)
Contributor Tongshu Zheng
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/licenses/by/4.0; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Other