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https://fromgistors.blogspot.com/p/remotior-sensus.html\n\n.. image:: https://img.shields.io/badge/Documentation-blue\n    :target: https://remotior-sensus.readthedocs.io\n\n.. image:: https://img.shields.io/badge/Bug%20reports-red\n    :target: https://github.com/semiautomaticgit/remotior_sensus/issues\n\n.. image:: https://img.shields.io/pypi/v/remotior-sensus?label=PyPI%20version\n    :target: https://pypi.org/project/remotior-sensus\n\n.. image:: https://img.shields.io/pypi/dm/remotior-sensus?label=PyPI%20downloads\n    :target: https://pypi.org/project/remotior-sensus\n\n.. image:: https://img.shields.io/conda/v/conda-forge/remotior-sensus?label=Conda%20version\n    :target: https://anaconda.org/conda-forge/remotior-sensus\n\n.. image:: https://img.shields.io/conda/d/conda-forge/remotior-sensus?label=Conda%20downloads\n    :target: https://anaconda.org/conda-forge/remotior-sensus\n\n.. image:: https://img.shields.io/conda/l/conda-forge/remotior-sensus\n    :target: https://www.gnu.org/licenses/\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10038132.svg\n    :target: https://doi.org/10.5281/zenodo.10038132\n\n.. image:: https://colab.research.google.com/assets/colab-badge.svg\n    :target: https://remotior-sensus.readthedocs.io/en/latest/quickstart.html\n\n\n\n\nIntroduction\n===========================================\n\nRemotior Sensus (which is Latin for \"a more remote sense\") is a Python package\nthat allows for the processing of remote sensing images and GIS data.\n\nRemotior Sensus is developed by Luca Congedo.\n\n- **Website:** https://fromgistors.blogspot.com/p/remotior-sensus.html\n- **Documentation:** https://remotior-sensus.readthedocs.io\n- **Source code:** https://github.com/semiautomaticgit/remotior_sensus\n- **Bug reports:** https://github.com/semiautomaticgit/remotior_sensus/issues\n\nThe main objective is to simplify the processing of remote sensing data\nthrough practical and integrated APIs that span from the download and\npreprocessing of satellite images to the postprocessing of classifications\nand GIS data.\nBasic dependencies are `NumPy \u003chttps://numpy.org\u003e`_,\n`SciPy \u003chttps://scipy.org\u003e`_ for calculations, and `GDAL \u003chttps://gdal.org/\u003e`_\nfor managing spatial data.\nOptionally, `Matplotlib` is used to display spectral signature plots.\n\nThe main features are:\n\n- **Search and Download** of remote sensing data such as Landsat and Sentinel-2.\n- **Preprocessing** of several products such as Landsat and Sentinel-2 images.\n- **Processing and postprocessing** tools to perform image classification through machine learning, manage GIS data and perform spatial analyses.\n- **Parallel processing** available for most processing tools.\n\n``WARNING: Remotior Sensus is still in early development;\nnew tools are going to be added, tools and APIs may change,\nand one may encounter issues and bugs using Remotior Sensus.``\n\nManagement of Raster Bands\n__________________________\n\nMost tools accept raster bands as input, defined through the file path.\n\nIn addition, raster bands can be managed through a catalog of BandSets,\nwhere each BandSet is an object that includes information about single bands\n(from the file path to the spatial and spectral characteristics).\nBands in a BandSet can be referenced by the properties thereof,\nsuch as order number or center wavelength.\n\nMultimple BandSets can be defined and identified by their reference number.\nTherefore, BandSets can be used as input for operations on multiple bands\nsuch as Principal Components Analysis, classification, mosaic,\nor band calculation.\n\nIn band calculations, alias name of bands based on center wavelength\n(e.g. blue, red) can be used to simplify the structure of calculation expression.\n\nPerformance\n___________\n\nMost tools are designed to run in parallel processes, through a simple\nand effective parallelization approach based on dividing the raster input\nin sections that are distributed to available threads, maximizing the use\nof available RAM.\nThis allows even complex algorithms to run in parallel.\nOptionally, the output file can be a virtual raster collecting the output\nrasters (corresponding to the sections) written independently by parallel\nprocesses; this avoids the time required to produce a unique raster output.\nMost tools allow for on the fly reprojection of input data.\n\nMachine Learning\n________________\n\nRemotior Sensus optional dependencies are `PyTorch \u003chttps://pytorch.org/\u003e`_\nand `scikit-learn \u003chttps://scikit-learn.org/stable/\u003e`_, which are\nintegrated in the classification tool.\nto allow for land cover classification through machine learning.\nThe aim is to simplify the training process and development of the model.\n\nInstallation\n______________\n\nRemotior Sensus requires `GDAL`, `NumPy` and `SciPy` for most functionalities.\nAlso, `scikit-learn` and `PyTorch` are optional but required for machine learning.\nOptionally, `Matplotlib` is used to display spectral signature plots.\n\nIt is recommended to install Remotior Sensus using a `Conda` environment.\n\n.. code-block:: console\n\n    $ conda install -c conda-forge remotior-sensus scikit-learn pytorch\n\n\n\nLicense of Remotior Sensus\n''''''''''''''''''''''''''\nRemotior Sensus is free software: you can redistribute it and/or modify it\nunder the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License,\nor (at your option) any later version.\nRemotior Sensus is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty\nof MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\nSee the GNU General Public License for more details.\nYou should have received a copy of the GNU General Public License\nalong with Remotior Sensus. If not, see https://www.gnu.org/licenses/.\n\n\nOfficial site\n_____________\n\nFor more information and tutorials visit the official site\n\n    `From GIS to Remote Sensing\n    \u003chttps://fromgistors.blogspot.com/p/remotior-sensus.html\u003e`_\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsemiautomaticgit%2Fremotior_sensus","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsemiautomaticgit%2Fremotior_sensus","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsemiautomaticgit%2Fremotior_sensus/lists"}