Hi,

I investigated your problem but I’m not able to reproduce it. I saw that the example is from upper left corner of the bounding box of the dataset ./data/train/train.shp from the GitHub repository. I ran the rasterization and there were no issues on the borders.

Is it possible that the way you plot the rasterized array produces errors and that the array itself is ok?

If this isn’t the case, then please provide more information that would allow us to fully reproduce the problem:

• the exact bounding box you are using,
• the exact size of raster image you produce,
• which OS are you using,
• are you using pip or conda based environment.

Hi,

Many thanks for taking a look into this issue.

Yes, I reduced my AOI to only the top left portion of the original dataset. Although, I have also tried with other parts of the original shapefile and get similar outcomes. I haven’t been able to try the entire dataset as it it quite large for the current tests I am doing.

I tried following the workflow similar to the example in this notebook (https://github.com/sentinel-hub/eo-learn/tree/master/examples/crop-type-classification.) but I still get the same raster border problem.

I plot the raster field as follows:

fig, ax = plt.subplots(figsize=(20, 12))
im = ax.imshow(eopatch.mask_timelesss'lulc_eroded'].squeeze(), cmap=lulc_cmap, norm=lulc_norm)
ax.set_aspect('auto')
ax.set_xticks(ticks=s])
ax.set_yticks(ticks=s])
cb = fig.colorbar(im, ax=fig.axes, orientation='horizontal', pad=0.03, aspect=100)
cb.ax.tick_params(labelsize=20) 
cb.set_ticks(sentry.id for entry in LULC])
cb.ax.set_xticklabels(sentry.class_name for entry in LULC], rotation=45, fontsize=15)

• The exact bounding box for my most recent AOI is: 603390.7041615268,6808384.23351457,609319.2351862685,6811495.355191282

• The size of the raster: not sure on the exact size, I split the AOI in to a 3x3 grid to get 9 patches.

• The OS I am using is: Ubuntu 20.04

• I am using a conda based environment. python-3.9.9 and eolearn-0.10.1 installed from conda-forge.

Thanks again for your help.

Thanks for providing more info. I can now confirm that the problem is in the plotting code and not in the rasterization process. An easy way to see this is to check unique values in the array you are plotting:

import numpy as np



np.unique(eopatch.mask_timeless['lulc_eroded'])

For your example above it should return only values array([0, 6, 8], dtype=uint8). Hence any other values were made up by matplotlib which is by default using interpolation for plotting. Therefore you can fix this by disabling the interpolation:

im = ax.imshow(

    eopatch.mask_timeless['lulc_eroded'].squeeze(),

    cmap=lulc_cmap,

    norm=lulc_norm,

    interpolation='none'

)

Hi,

Thank you so much for the solution, it was the interpolation that was causing the border value changes.