UKIS Cloud Shadow MASK (ukis-csmask) package masks clouds and cloud shadows in Sentinel-2, Landsat-9, Landsat-8, Landsat-7 and Landsat-5 images. Masking is performed with a pre-trained convolution neural network. It is fast and works directly on Level-1C data (no atmospheric correction required). Images just need to be in Top Of Atmosphere (TOA) reflectance and include at least the "blue", "green", "red" and "nir" spectral bands. Best performance (in terms of accuracy and speed) is achieved when images also include "swir16" and "swir22" spectral bands and are resampled to approximately 30 m spatial resolution.
This publication provides further insight into the underlying algorithm and compares it to the widely used Fmask algorithm across a heterogeneous test dataset.
Wieland, M.; Li, Y.; Martinis, S. Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network. Remote Sensing of Environment, 2019, 230, 1-12. https://doi.org/10.1016/j.rse.2019.05.022
This publication introduces the Python package, performs additional evaluation on recent cloud and cloud shadow benchmark datasets and tests the applicability of ukis-csmask on Landsat-9 imagery.
Wieland, M.; Fichtner, F.; Martinis, S. UKIS-CSMASK: A Python package for multi-sensor cloud and cloud shadow segmentation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 2022, XLIII-B3-2022, 217–222. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-217-2022
If you use ukis-csmask in your work, please consider citing one of the above publications.
Here's an example on how to compute a cloud and cloud shadow mask from an image. Please note that here we use ukis-pysat for convencience image handling, but you can also work directly with numpy arrays.
from ukis_csmask.mask import CSmask
from ukis_pysat.raster import Image, Platform
# read Level-1C image from file, convert digital numbers to TOA reflectance
# and make sure resolution is 30 m to get best performance
img = Image(data="sentinel2.tif", dimorder="last")
img.dn2toa(platform=Platform.Sentinel2)
img.warp(
resampling_method=0,
resolution=30,
dst_crs=img.dataset.crs
)
# compute cloud and cloud shadow mask
# NOTE: band_order must match the order of bands in the input image. it does not have to be in this explicit order.
# make sure to use these six spectral bands to get best performance
csmask = CSmask(
img=img.arr,
band_order=["blue", "green", "red", "nir", "swir16", "swir22"],
nodata_value=0,
)
# access cloud and cloud shadow mask
csmask_csm = csmask.csm
# access valid mask
csmask_valid = csmask.valid
# convert results to UKIS-pysat Image
csmask_csm = Image(csmask.csm, transform=img.dataset.transform, crs=img.dataset.crs, dimorder="last")
csmask_valid = Image(csmask.valid, transform=img.dataset.transform, crs=img.dataset.crs, dimorder="last")
# write results back to file
csmask_csm.write_to_file("sentinel2_csm.tif", dtype="uint8", compress="PACKBITS")
csmask_valid.write_to_file("sentinel2_valid.tif", dtype="uint8", compress="PACKBITS", kwargs={"nbits":2})Here's a similar example based on Landsat 8.
import rasterio
import numpy as np
from ukis_csmask.mask import CSmask
from ukis_pysat.raster import Image, Platform
# set Landsat 8 source path and prefix (example)
data_path = "/your_data_path/"
L8_file_prefix = "LC08_L1TP_191015_20210428_20210507_02_T1"
data_path = data_path+L8_file_prefix+"/"
mtl_file = data_path+L8_file_prefix+"_MTL.txt"
# stack [B2:'Blue', B3:'Green', B4:'Red', B5:'NIR', B6:'SWIR1', B7:'SWIR2'] as numpy array
L8_band_files = [data_path+L8_file_prefix+'_B'+ x + '.TIF' for x in [str(x+2) for x in range(6)]]
# >> adopted from https://gis.stackexchange.com/questions/223910/using-rasterio-or-gdal-to-stack-multiple-bands-without-using-subprocess-commands
# read metadata of first file
with rasterio.open(L8_band_files[0]) as src0:
meta = src0.meta
# update meta to reflect the number of layers
meta.update(count = len(L8_band_files))
# read each layer and append it to numpy array
L8_bands = []
for id, layer in enumerate(L8_band_files, start=1):
with rasterio.open(layer) as src1:
L8_bands.append(src1.read(1))
L8_bands = np.stack(L8_bands,axis=2)
# <<
img = Image(data=L8_bands, crs = meta['crs'], transform = meta['transform'], dimorder="last")
img.dn2toa(
platform=Platform.Landsat8,
mtl_file=mtl_file,
wavelengths = ["blue", "green", "red", "nir", "swir16", "swir22"]
)
# >> proceed by analogy with Sentinel 2 exampleThe easiest way to install ukis-csmask is through pip. To install ukis-csmask with default CPU provider run the following.
pip install ukis-csmask[cpu]To install ukis-csmask with OpenVino support for enhanced CPU inference run the following instead.
pip install ukis-csmask[openvino]To install ukis-csmask with GPU support run the following instead. This requires that you have a GPU with CUDA runtime libraries installed on the system.
pip install ukis-csmask[gpu]ukis-csmask depends on onnxruntime. For a list of additional dependencies check the requirements.
The UKIS team creates and adapts libraries which simplify the usage of satellite data. Our team includes (in alphabetical order):
- Boehnke, Christian
- Fichtner, Florian
- Mandery, Nico
- Martinis, Sandro
- Riedlinger, Torsten
- Wieland, Marc
German Aerospace Center (DLR)
This software is licensed under the Apache 2.0 License.
Copyright (c) 2020 German Aerospace Center (DLR) * German Remote Sensing Data Center * Department: Geo-Risks and Civil Security
See changelog.
The UKIS team welcomes contributions from the community. For more detailed information, see our guide on contributing if you're interested in getting involved.
The DLR project Environmental and Crisis Information System (the German abbreviation is UKIS, standing for Umwelt- und Kriseninformationssysteme aims at harmonizing the development of information systems at the German Remote Sensing Data Center (DFD) and setting up a framework of modularized and generalized software components.
UKIS is intended to ease and standardize the process of setting up specific information systems and thus bridging the gap from EO product generation and information fusion to the delivery of products and information to end users.
Furthermore the intention is to save and broaden know-how that was and is invested and earned in the development of information systems and components in several ongoing and future DFD projects.