Examples in docstring
@@ -319,6 +319,22 @@ class AddMultiCloudMaskTask(EOTask):
Resizing is performed with linear interpolation. After classification, the cloud
probabilities are themselves upscaled to the original dimensions, before proceeding
with masking operations.
+
+ Example usage:
+ ```python
+ # Only output the combined mask
+ task1 = AddMultiCloudMaskTask(processing_resolution='120m',
+ mask_feature='CLM_INTERSSIM',
+ average_over=16,
+ dilation_size=8)
+
+ # Only output monotemporal masks. Only monotemporal processing is done.
+ task2 = AddMultiCloudMaskTask(processing_resolution='120m',
+ mono_features=(None, 'CLM_S2C'),
+ mask_feature=None,
+ average_over=16,
+ dilation_size=8)
+ ```
"""
def __init__(self,
@@ -382,8 +398,9 @@ class AddMultiCloudMaskTask(EOTask):
added to `eopatch.mask`. By default, none of them are added.
:type multi_features: (str | None, str | None)
:param mask_feature: Name of the output intersection feature. The masks are added to the `eopatch.mask`
- attribute dictionary. Default value: `'CLM_INTERSSIM'`.
- :type mask_feature: str
+ attribute dictionary. Default value: `'CLM_INTERSSIM'`. If None, the intersection
+ feature is not computed.
+ :type mask_feature: str | None
:param mono_threshold: Cloud probability threshold for the mono classifier. Default value: `0.4`.
:type mono_threshold: float
:param multi_threshold: Cloud probability threshold for the multi classifier. Default value: `0.5`.
