Changelog

May 2, 2026 · View on GitHub

All notable changes to MultiClean are documented here.

Unreleased

Performance. clean_array is substantially faster on multi-class inputs. On a 15669×18633 / 147-class land-use raster, end-to-end runtime dropped from ~85 s to ~40 s. On the 8011×7901 / 4-class Landsat cloud-and-shadow example, runtime dropped from ~2.5 s to ~1.1 s. Wins came from:
- Replacing the float32 smoothed-labels buffer with a uint8/uint16 class-code array (selected automatically based on class count). The per-class equality scan is 2-4× cheaper in memory bandwidth.
- Combining per-class small-island masks in flight instead of accumulating all K of them first.
- Filling invalid pixels in place rather than allocating a copy.
- Replacing scipy.ndimage.distance_transform_edt with cv2.distanceTransformWithLabels for the nearest-valid fill (~3.4× faster on the fill stage). Both algorithms produce mathematically equivalent output (the same minimum L2 distance); they differ only in which equidistant source pixel wins a tie.
dtype preservation. The output now strictly matches the input dtype. Previously the pipeline routed everything through float32 internally, which silently downcast float64 inputs and rounded int32 values larger than 2²⁴ (and int64 values larger than 2⁵³).

All-NaN float input with fill_nan=True now deterministically returns an all-NaN array. The previous code relied on whatever value np.empty happened to leave in the sentinel slot.
Large integer class values (int32 > 2²⁴, int64 > 2⁵³) are now preserved bit-exactly, instead of being silently rounded by the internal float32 round-trip.

Dropped the scipy runtime dependency. cv2 (already a runtime dependency) now handles the distance-transform fill.

fill_nan option on clean_array: when True, NaN values in float input arrays are filled from the nearest valid pixel rather than preserved as nodata.

Initial public release.
clean_array API for morphological cleaning of multi-class 2D arrays: per-class edge smoothing (morphological opening), per-class small-island removal (connected components), and gap filling using nearest-valid via Euclidean distance transform.
Documentation: README, two example notebooks (land use, cloud shadow), and a Google Colab tutorial notebook.