{ "info": { "author": "Berend Weel, Elena Ranguelova, Bouwe Andela, Maximilian Filtenborg, Derk Barten, Yifat Dzigan, Ronald van Haren, Niels Drost", "author_email": "b.weel@esiencecenter.nl", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "Satsense\n========\n\n|Build Status| |Codacy Badge| |Maintainability| |Test Coverage|\n|Documentation Status| |DOI|\n\nSatsense is an open source Python library for patch based land-use and\nland-cover classification, initially developed for a project on deprived\nneighborhood detection. However, many of the algorithms made available\nthrough Satsense can be applied in other domains, such as ecology and\nclimate science.\n\nSatsense is based on readily available open source libraries, such as\nopencv for machine learning and the rasterio/gdal and netcdf libraries\nfor data access. It has a modular design that makes it easy to add your\nown hand-crafted feature or use deep learning instead.\n\nDetection of deprived neighborhoods is a land-use classification problem\nthat is traditionally solved using hand crafted features like HoG,\nLacunarity, NDXI, Pantex, Texton, and SIFT, computed from very high\nresolution satellite images. One of the goals of Satsense is to\nfacilitate assessing the performance of these features on practical\napplications. To achieve this Satsense provides an easy to use open\nsource reference implementation for these and other features, as well as\nfacilities to distribute feature computation over multiple cpu\u2019s. In the\nfuture the library will also provide easy access to metrics for\nassessing algorithm performance.\n\n- satsense - library for analysing satellite images, performance\n evaluation, etc.\n- notebooks - IPython notebooks for illustrating and testing the usage\n of Satsense\n\nWe are using python 3.6/3.7 and jupyter notebook for our code.\n\nDocumentation\n-------------\nCan be found on `readthedocs `__.\n\nInstallation\n------------\n\nPlease see the `installation guide on readthedocs `__.\n\nContributing\n------------\n\nContributions are very welcome! Please see\n`CONTRIBUTING.md `__\nfor our contribution guidelines.\n\nCiting Satsense\n---------------\n\nIf you use Satsense for scientific research, please cite it. You can\ndownload citation files from\n`research-software.nl `__.\n\nReferences\n----------\n\nThe collection of algorithms made available trough this package is\ninspired by\n\n J. Graesser, A. Cheriyadat, R. R. Vatsavai, V. Chandola,\n J. Long and E. Bright, \"Image Based Characterization of Formal and\n Informal Neighborhoods in an Urban Landscape\", in IEEE Journal of\n Selected Topics in Applied Earth Observations and Remote Sensing,\n vol.\u00a05, no. 4, pp.\u00a01164-1176, Aug.\u00a02012. doi:\n 10.1109/JSTARS.2012.2190383\n\nJordan Graesser himself also maintains `a\nlibrary `__ with many of these\nalgorithms.\n\nTest Data\n~~~~~~~~~\n\nThe test data has been extracted from the Copernicus Sentinel data 2018.\n\n.. |Build Status| image:: https://travis-ci.com/DynaSlum/satsense.svg?branch=master\n :target: https://travis-ci.com/DynaSlum/satsense\n.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/458c8543cd304b8387b7b114218dc57c\n :target: https://www.codacy.com/app/DynaSlum/satsense?utm_source=github.com&utm_medium=referral&utm_content=DynaSlum/satsense&utm_campaign=Badge_Grade\n.. |Maintainability| image:: https://api.codeclimate.com/v1/badges/ed3655f6056f89f5e107/maintainability\n :target: https://codeclimate.com/github/DynaSlum/satsense/maintainability\n.. |Test Coverage| image:: https://api.codeclimate.com/v1/badges/ed3655f6056f89f5e107/test_coverage\n :target: https://codeclimate.com/github/DynaSlum/satsense/test_coverage\n.. |Documentation Status| image:: https://readthedocs.org/projects/satsense/badge/?version=latest\n :target: https://satsense.readthedocs.io/en/latest/?badge=latest\n.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1463015.svg\n :target: https://doi.org/10.5281/zenodo.1463015\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/DynaSlum/SateliteImaging", "keywords": "", "license": "Apache Software License", "maintainer": "", "maintainer_email": "", "name": "satsense", "package_url": "https://pypi.org/project/satsense/", "platform": "any", "project_url": "https://pypi.org/project/satsense/", "project_urls": { "Homepage": "https://github.com/DynaSlum/SateliteImaging" }, "release_url": "https://pypi.org/project/satsense/0.9/", "requires_dist": [ "affine", "descartes", "fiona", "netCDF4", "numpy", "opencv-contrib-python-headless (<3.4.3)", "rasterio", "scikit-image (>=0.14.2)", "scikit-learn", "scipy", "shapely", "hypothesis[numpy] ; 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