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=(37, 70, 73, 1), dtype=float32)
MY_S2_BANDS: numpy.ndarray(shape=(37, 70, 73, 9), dtype=uint8)
RPI: numpy.ndarray(shape=(37, 70, 73, 1), dtype=float64)
}
mask: {
CLM: numpy.ndarray(shape=(37, 70, 73, 1), dtype=bool)
IS_DATA: numpy.ndarray(shape=(37, 70, 73, 1), dtype=bool)
SCL: numpy.ndarray(shape=(37, 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
