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I am using land use regression with machine learning to create an air pollution model. However, almost all tiles I can find using Python and Folium contain text, which my intuition says cannot possibly be a good thing. I've seen papers use drawn maps for this task, and I am not convinced they are better than satellite images...

Does anybody here have experience with extracting city maps for air pollution modelling? Which ones should I use? Is satellite data better than drawn maps?

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  • $\begingroup$ Could you provide a bit more detail in the form of some examples? What is your exact question, and what is wrong with the status quo? How about land.copernicus.eu/global/products/lai ? $\endgroup$
    – Erik
    Nov 1, 2021 at 9:19
  • $\begingroup$ @Erik I am writing an answer to my own question right now. Hopefully that answer will clarify what I was looking for. Also, I'm open for other answers. $\endgroup$
    – Avatrin
    Nov 1, 2021 at 9:35
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    $\begingroup$ So, if I get your question correct, you want to model air pollution, with as input a land use cover map, and you need a product that gives you land use? So [input data] -> land use -> air pollution? If it needs to be a European product only, I think you cannot beat land.copernicus.eu/pan-european/corine-land-cover which already gives you the land use data in either 300x300 m resolution, or even full vector format, without any text. These maps are compiled by local authorities. $\endgroup$
    – Erik
    Nov 1, 2021 at 10:55

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Satellite data is more noisy than rasterized vector maps; Steininger et al found that OpenStreetMap input was superior for their CNN-based land use regression model. They tested OpenStreetMap, Google Maps, Satellite images from Google maps and a combination of OpenStreetMap and Google Maps Satellite. The model trained on inputs from OpenStreetMap outperformed all of the others.

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