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@zigaLuksic zigaLuksic released this 06 Sep 15:07
· 54 commits to master since this release
v1.5.0
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Learn about vigilant mode.

The release focuses on making eo-learn much simpler to install, reducing the number of dependencies, and improving validation of soundness of EOPatch data.

  • eo-learn is now distributed as a single package. Installation of eo-learn-mask and similar is no longer necessary and users are warned when such installations are detected.
  • Changes to timestamps and bbox attributes of EOPatch objects:
    • FeatureType.TIMESTAMPS and FeatureType.BBOX have been deprecated, data should be accessed via attributes. Feature parsers no longer return these values (for instance when calling EOPatch.get_features).
    • EOPatches without temporal information now have a timestamp value of None, whereas a timestamp value [] signifies that the EOPatch has a temporal dimension of 0.
    • Introduced a get_timestamps method that will fail if timestamps are None. This can be used in cases where timestamps are assumed to be present (to avoid issues with type-checking and ill formed inputs).
    • Loading, saving, and copying of EOPatches will take timestamps into account either when processing the full eopatch (i.e. features=...) or if the selection contains a temporal feature. The behavior can be controlled via the load_timestamps/save_timestamps/copy_timestamps parameter.
  • Saving and loading of FeatureType.META_INFO now processes each feature as a separate file, allowing better filtering and preventing accidental overwriting.
  • The default backend for SpatialResizeTask has been switched to cv2 to reduce the number of dependencies.
  • eolearn.geometry.morphology tasks now use cv2 instead of scikit-image to reduce the number of dependencies. The task interfaces have been slightly adjusted.
  • Improved reports:
    • Exception grouping is now done by exception origin instead of exception message, resulting in shorter reports.
    • Added execution time statistics per node
  • CloudMaskTask has been restricted to mono-temporal predictions using the s2cloudless package. For the multi-temporal one check here.
  • Certain tasks (for instance SaveTask and LoadTask) no longer pass arguments to the super-class via **kwargs in order to improve documentation and type-checking.
  • SaveTask and LoadTask now raise OSError exceptions instead of IOError.
  • Project-specific and outdated EOTasks were moved to extras or to the example repository eo-learn-examples/extra-tasks.
  • The submodule eolearn.features.bands_extraction has been renamed to eolearn.features.ndi.
  • The submodule eolearn.ml_tools.extra.plotting has been moved to eolearn.visualization.utils.
  • Compression of EOPatch files has been hardcoded. The parameter compression_level has been deprecated and has no effect.
  • Introduced experimental zarr support for loading/saving temporal slices of temporal features. The API might be changed in future releases.
  • Limited rasterio to 1.3.7 due to an issue with importing rasters from AWS S3
  • Updated examples, simplified tests, various improvements.
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