Metadata-Version: 2.1
Name: satsense
Version: 0.9
Summary: Library for multispectral remote imaging.
Home-page: https://github.com/DynaSlum/SateliteImaging
Author: Berend Weel, Elena Ranguelova, Bouwe Andela, Maximilian Filtenborg, Derk Barten, Yifat Dzigan, Ronald van Haren, Niels Drost
Author-email: b.weel@esiencecenter.nl
License: Apache Software License
Description: Satsense
        ========
        
        |Build Status| |Codacy Badge| |Maintainability| |Test Coverage|
        |Documentation Status| |DOI|
        
        Satsense is an open source Python library for patch based land-use and
        land-cover classification, initially developed for a project on deprived
        neighborhood detection. However, many of the algorithms made available
        through Satsense can be applied in other domains, such as ecology and
        climate science.
        
        Satsense is based on readily available open source libraries, such as
        opencv for machine learning and the rasterio/gdal and netcdf libraries
        for data access. It has a modular design that makes it easy to add your
        own hand-crafted feature or use deep learning instead.
        
        Detection of deprived neighborhoods is a land-use classification problem
        that is traditionally solved using hand crafted features like HoG,
        Lacunarity, NDXI, Pantex, Texton, and SIFT, computed from very high
        resolution satellite images. One of the goals of Satsense is to
        facilitate assessing the performance of these features on practical
        applications. To achieve this Satsense provides an easy to use open
        source reference implementation for these and other features, as well as
        facilities to distribute feature computation over multiple cpu’s. In the
        future the library will also provide easy access to metrics for
        assessing algorithm performance.
        
        -  satsense - library for analysing satellite images, performance
           evaluation, etc.
        -  notebooks - IPython notebooks for illustrating and testing the usage
           of Satsense
        
        We are using python 3.6/3.7 and jupyter notebook for our code.
        
        Documentation
        -------------
        Can be found on `readthedocs <https://satsense.readthedocs.io>`__.
        
        Installation
        ------------
        
        Please see the `installation guide on readthedocs <https://satsense.readthedocs.io/en/latest/installation.html#installation>`__.
        
        Contributing
        ------------
        
        Contributions are very welcome! Please see
        `CONTRIBUTING.md <https://github.com/DynaSlum/satsense/blob/master/CONTRIBUTING.md>`__
        for our contribution guidelines.
        
        Citing Satsense
        ---------------
        
        If you use Satsense for scientific research, please cite it. You can
        download citation files from
        `research-software.nl <https://www.research-software.nl/software/satsense>`__.
        
        References
        ----------
        
        The collection of algorithms made available trough this package is
        inspired by
        
            J. Graesser, A. Cheriyadat, R. R. Vatsavai, V. Chandola,
            J. Long and E. Bright, "Image Based Characterization of Formal and
            Informal Neighborhoods in an Urban Landscape", in IEEE Journal of
            Selected Topics in Applied Earth Observations and Remote Sensing,
            vol. 5, no. 4, pp. 1164-1176, Aug. 2012. doi:
            10.1109/JSTARS.2012.2190383
        
        Jordan Graesser himself also maintains `a
        library <https://github.com/jgrss/spfeas>`__ with many of these
        algorithms.
        
        Test Data
        ~~~~~~~~~
        
        The test data has been extracted from the Copernicus Sentinel data 2018.
        
        .. |Build Status| image:: https://travis-ci.com/DynaSlum/satsense.svg?branch=master
           :target: https://travis-ci.com/DynaSlum/satsense
        .. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/458c8543cd304b8387b7b114218dc57c
           :target: https://www.codacy.com/app/DynaSlum/satsense?utm_source=github.com&utm_medium=referral&utm_content=DynaSlum/satsense&utm_campaign=Badge_Grade
        .. |Maintainability| image:: https://api.codeclimate.com/v1/badges/ed3655f6056f89f5e107/maintainability
           :target: https://codeclimate.com/github/DynaSlum/satsense/maintainability
        .. |Test Coverage| image:: https://api.codeclimate.com/v1/badges/ed3655f6056f89f5e107/test_coverage
           :target: https://codeclimate.com/github/DynaSlum/satsense/test_coverage
        .. |Documentation Status| image:: https://readthedocs.org/projects/satsense/badge/?version=latest
           :target: https://satsense.readthedocs.io/en/latest/?badge=latest
        .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1463015.svg
           :target: https://doi.org/10.5281/zenodo.1463015
        
Platform: any
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: notebooks
Provides-Extra: dev
