Dear Sentinelhub-Team,

I experienced a problem by using some of your eo-learn functions:

from eolearn.io import AddSen2CorClassificationFeature

from eolearn.mask import AddCloudMaskTask, get_s2_pixel_cloud_detector

By executing the following EO-Tasks, I retrieve different shapes within the resulting EO-Patch:

# Add SCL from sen2cor

add_SCL = AddSen2CorClassificationFeature(sen2cor_classification='SCL',

                                          layer='TRUE-COLOR-S2-L2A',

                                          image_format=MimeType.TIFF_d32f,

                                          instance_id=instance_id)



# Add Sen2Cloudless

cloud_classifier = get_s2_pixel_cloud_detector(average_over=2,

                                               dilation_size=1,

                                               all_bands=False)

add_clm = AddCloudMaskTask(cloud_classifier, 'BANDS-S2CLOUDLESS',

                           cm_size_y='80m', cm_size_x='80m',

                           cmask_feature='CLM',  # cloud mask name

                           cprobs_feature='CLP',  # cloud prob. map name

                           instance_id=instance_id

                           )

When printing the resulting EO-Patch, I get the following:

EOPatch(

  data: {

    CLP: numpy.ndarray(shape=(81, 70, 73, 1), dtype=float32)

    MY_S2_BANDS: numpy.ndarray(shape=(81, 70, 73, 9), dtype=uint8)

    RPI: numpy.ndarray(shape=(81, 70, 73, 1), dtype=float64)

  }

  mask: {

    CLM: numpy.ndarray(shape=(81, 70, 73, 1), dtype=bool)

    IS_DATA: numpy.ndarray(shape=(81, 70, 73, 1), dtype=bool)

    SCL: numpy.ndarray(shape=(81, 70, 72, 1), dtype=int32)

  }

  scalar: {}

  label: {}

  vector: {}

  data_timeless: {}

  mask_timeless: {}

  scalar_timeless: {}

  label_timeless: {}

  vector_timeless: {}

  meta_info: {

    maxcc: 1.0

    service_type: 'wcs'

    size_x: '10m'

    size_y: '10m'

    time_difference: datetime.timedelta(seconds=3600)

    time_interval: ['2019-05-01', '2019-07-30']

  }

  bbox: BBox(((622033.5364121953, 5352274.161972809), (622762.6876688242, 5352975.174904556)), crs=CRS('32633'))

  timestamp: [datetime.datetime(2019, 5, 1, 10, 7, 19), ..., datetime.datetime(2019, 7, 30, 10, 7, 20)], length=37

)

Here you can see that mask.CLM and mask.SCL have slightly different shapes.

CLM seems to have the same shape than the rest of the “data”, but SCL seems to have one column less.

Is this problem on your or on my side and if I can avoid it, can you tell me how?

Best,

Johannes