Metadata-Version: 2.1
Name: ukis-csmask
Version: 0.0.1
Summary: generic classes and functions to query, access and process multi-spectral and SAR satellite images
Home-page: https://github.com/dlr-eoc/ukis-csmask
Author: German Aerospace Center (DLR)
Author-email: ukis-helpdesk@dlr.de
License: Apache 2.0
Description: # [![UKIS](img/ukis-logo.png)](https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5413/10560_read-21914/) ukis-csmask
        
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        UKIS Cloud Shadow MASK (ukis-csmask) package masks clouds and cloud shadows in Sentinel-2, 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", "NIR", "SWIR1" and "SWIR2" spectral bands.
        
        This [publication](https://doi.org/10.1016/j.rse.2019.05.022) provides further insight into the underlying algorithm and compares it to the widely used [Fmask](http://www.pythonfmask.org/en/latest/) algorithm across a heterogeneous test dataset. If you use ukis-csmask in your work, please consider citing us as follows.
        > 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](https://doi.org/10.1016/j.rse.2019.05.022)
        
        ![Examples](img/examples.png)
        
        ## Example
        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](https://github.com/dlr-eoc/ukis-pysat) for convencience image handling, but you can also work directly with NUMPY arrays.
        
        ````python
        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,
        # but needs to include these six spectral bands.
        csmask = CSmask(
            img=img.arr,
            band_order=["Blue", "Green", "Red", "NIR", "SWIR1", "SWIR2"],
            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=np.uint8, compress="PACKBITS")
        csmask_valid.write_to_file("sentinel2_valid.tif", dtype=np.uint8, compress="PACKBITS", kwargs={"nbits":2})
        ````
        
        ## Installation
        The easiest way to install ukis-csmask is through pip.
        
        ```shell
        pip install ukis-csmask
        ```
        
        For a list of dependencies check the [requirements](https://github.com/dlr-eoc/ukis-csmask/blob/main/requirements.txt).
        
        ## Contributors
        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)
        
        ## Licenses
        This software is licensed under the [Apache 2.0 License](https://github.com/dlr-eoc/ukis-csmask/blob/main/LICENSE).
        
        Copyright (c) 2020 German Aerospace Center (DLR) * German Remote Sensing Data Center * Department: Geo-Risks and Civil Security
        
        ## Changelog
        See [changelog](https://github.com/dlr-eoc/ukis-csmask/blob/main/CHANGELOG.rst).
        
        ## Contributing
        The UKIS team welcomes contributions from the community.
        For more detailed information, see our guide on [contributing](https://github.com/dlr-eoc/ukis-csmask/blob/main/CONTRIBUTING.md) if you're interested in getting involved.
        
        ## What is UKIS?
        The DLR project Environmental and Crisis Information System (the German abbreviation is UKIS, standing for [Umwelt- und Kriseninformationssysteme](https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5413/10560_read-21914/) 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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: GIS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: gpu
Provides-Extra: dev
