Geographic Map Generation Needs Improvement

[TreacherousDev]

The current implementation of the typhoon animation engine uses a custom map creator library built with Cartopy and NaturalEarth. This library generates geographical features like land and ocean borders, rivers, lakes, and country borders by using these functions:

# Add Land and Ocean Features
ax.add_feature(cfeature.NaturalEarthFeature('physical', 'land', '10m', facecolor='darkgreen'))
ax.add_feature(cfeature.NaturalEarthFeature('physical', 'ocean', '10m', facecolor=rgb_to_normalized(0, 0, 70)))
ax.add_feature(cfeature.BORDERS, linestyle=':', edgecolor='black', linewidth=1)
ax.add_feature(cfeature.RIVERS, edgecolor=rgb_to_normalized(0, 0, 70), linewidth=0.5)
ax.add_feature(cfeature.LAKES, facecolor=rgb_to_normalized(0, 0, 70))

Hoewver, the satelite image is generated by overlaying a low-quality satellite image and rendering it with a semi-transparent effect for the background.

ax.stock_img(zorder=3).set_alpha(0.35)

While functional, this method isn't the most efficient or accurate approach for creating geographically precise maps, as it turns out to be pixelated.

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Problem:

• The current approach relies on low-quality stock images to render geographical features, which reduces the accuracy and appearance of the map.

Expected Solution:

• Explore better methods for creating more accurate and higher-quality maps that still make use of libraries like Cartopy.
• Consider baking the geographical data into the map instead of overlaying a stock image.

Request:

• I am looking for suggestions on improving the map's geographical accuracy and eliminating the need for overlaying a low-resolution satellite image.
• Any feedback or references to better techniques within Cartopy, Matplotlib, or other advanced geospatial mapping libraries would be appreciated.

[KurooNxtsu]

How about using SAR(Synthetic-aperture radar) images and integrating them with cartopy? I could work on this if need more help!