<p>Satellite data is more noisy than rasterized vector maps; <a href="https://arxiv.org/pdf/2002.07493.pdf" rel="nofollow noreferrer">Steininger et al found that</a> 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.</p>

CC BY-SA 4.0
2021-11-01T09:44:57.540Z
2021-11-01T09:44:57.540Z
Avatrin
<p>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...</p>
<p>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?</p>

CC BY-SA 4.0
2021-11-01T09:19:56.670Z
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 https://land.copernicus.eu/global/products/lai ?
Erik
CC BY-SA 4.0
2021-11-01T09:35:59.193Z
@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.
Avatrin
CC BY-SA 4.0
2021-11-01T10:55:29.240Z
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 https://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.
Erik
<air-pollution><mapping><python>
Folium/Leaflet tiles for air pollution modelling
Avatrin
# Folium/Leaflet tiles for air pollution modelling

(Asked by: username_0 on 2021-10-30)

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?

**Tags:** air-pollution, mapping, python

> **Comments:**
> **username_1**: 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 https://land.copernicus.eu/global/products/lai ?  
> **username_0**: @username_1 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.  
> **username_1**: 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 https://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.  

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## 1 answers

### Answer by username_0 on 2021-11-01. Score: 1 | Accepted answer

Satellite data is more noisy than rasterized vector maps; [Steininger et al found that](https://arxiv.org/pdf/2002.07493.pdf) 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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