OmniCloudMask
State-of-the-art cloud and cloud shadow segmentation for satellite imagery
OmniCloudMask is a Python library for cloud and cloud shadow detection in high to moderate resolution satellite imagery. It supports resolutions from 10 m to 50 m and works with imagery from Sentinel-2, Landsat, PlanetScope, Maxar, and other sensors with Red, Green, and NIR bands.
Key Features
Works with any imagery containing Red, Green, and NIR bands (10 m to 50 m resolution)
Any processing level (L1C, L2A, TOA, surface reflectance, etc.)
Validated on Sentinel-2, Landsat 8, PlanetScope, and Maxar imagery
Supports CUDA, MPS (Apple Silicon), and CPU inference
Optional confidence map export
Fast inference with multi-threaded patch-based processing
Output Classes
OmniCloudMask produces segmentation masks with four classes defined by the CloudSEN12 dataset:
Value |
Class |
|---|---|
0 |
Clear |
1 |
Thick Cloud |
2 |
Thin Cloud |
3 |
Cloud Shadow |