Skip to main content

Remotior Sensus is software to process remote sensing and GIS data

Project description

https://img.shields.io/badge/Website-darkgreen https://img.shields.io/badge/Documentation-blue https://img.shields.io/badge/Bug%20reports-red https://img.shields.io/pypi/v/remotior-sensus?label=PyPI%20version https://img.shields.io/pypi/dm/remotior-sensus?label=PyPI%20downloads https://img.shields.io/conda/v/conda-forge/remotior-sensus?label=Conda%20version https://img.shields.io/conda/d/conda-forge/remotior-sensus?label=Conda%20downloads https://img.shields.io/conda/l/conda-forge/remotior-sensus https://zenodo.org/badge/DOI/10.5281/zenodo.10038132.svg https://colab.research.google.com/assets/colab-badge.svg

Introduction

Remotior Sensus (which is Latin for “a more remote sense”) is a Python package that allows for the processing of remote sensing images and GIS data.

Remotior Sensus is developed by Luca Congedo.

The main objective is to simplify the processing of remote sensing data through practical and integrated APIs that span from the download and preprocessing of satellite images to the postprocessing of classifications and GIS data. Basic dependencies are NumPy, SciPy for calculations, and GDAL for managing spatial data. Optionally, Matplotlib is used to display spectral signature plots.

The main features are:

  • Search and Download of remote sensing data such as Landsat and Sentinel-2.

  • Preprocessing of several products such as Landsat and Sentinel-2 images.

  • Processing and postprocessing tools to perform image classification through machine learning, manage GIS data and perform spatial analyses.

  • Parallel processing available for most processing tools.

WARNING: Remotior Sensus is still in early development; new tools are going to be added, tools and APIs may change, and one may encounter issues and bugs using Remotior Sensus.

Management of Raster Bands

Most tools accept raster bands as input, defined through the file path.

In addition, raster bands can be managed through a catalog of BandSets, where each BandSet is an object that includes information about single bands (from the file path to the spatial and spectral characteristics). Bands in a BandSet can be referenced by the properties thereof, such as order number or center wavelength.

Multimple BandSets can be defined and identified by their reference number. Therefore, BandSets can be used as input for operations on multiple bands such as Principal Components Analysis, classification, mosaic, or band calculation.

In band calculations, alias name of bands based on center wavelength (e.g. blue, red) can be used to simplify the structure of calculation expression.

Performance

Most tools are designed to run in parallel processes, through a simple and effective parallelization approach based on dividing the raster input in sections that are distributed to available threads, maximizing the use of available RAM. This allows even complex algorithms to run in parallel. Optionally, the output file can be a virtual raster collecting the output rasters (corresponding to the sections) written independently by parallel processes; this avoids the time required to produce a unique raster output. Most tools allow for on the fly reprojection of input data.

Machine Learning

Remotior Sensus optional dependencies are PyTorch and scikit-learn, which are integrated in the classification tool. to allow for land cover classification through machine learning. The aim is to simplify the training process and development of the model.

Installation

Remotior Sensus requires GDAL, NumPy and SciPy for most functionalities. Also, scikit-learn and PyTorch are optional but required for machine learning. Optionally, Matplotlib is used to display spectral signature plots.

It is recommended to install Remotior Sensus using a Conda environment.

$ conda install -c conda-forge remotior-sensus scikit-learn pytorch

License of Remotior Sensus

Remotior Sensus is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. Remotior Sensus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Remotior Sensus. If not, see https://www.gnu.org/licenses/.

Official site

For more information and tutorials visit the official site

From GIS to Remote Sensing

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

remotior_sensus-0.7.7.tar.gz (500.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

remotior_sensus-0.7.7-py3-none-any.whl (362.7 kB view details)

Uploaded Python 3

File details

Details for the file remotior_sensus-0.7.7.tar.gz.

File metadata

  • Download URL: remotior_sensus-0.7.7.tar.gz
  • Upload date:
  • Size: 500.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for remotior_sensus-0.7.7.tar.gz
Algorithm Hash digest
SHA256 4f7f54dd052775c8ee08cf055b01e35d3412136b00ab3284c8b750773f16607c
MD5 18489e55b9ddca4c755f880c1b8fd1c5
BLAKE2b-256 23743b08a6b716c7ca9793f1c729d39610cd9f7369b8e17ca1219c513586768c

See more details on using hashes here.

Provenance

The following attestation bundles were made for remotior_sensus-0.7.7.tar.gz:

Publisher: publish-to-pypi.yml on semiautomaticgit/remotior_sensus

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file remotior_sensus-0.7.7-py3-none-any.whl.

File metadata

  • Download URL: remotior_sensus-0.7.7-py3-none-any.whl
  • Upload date:
  • Size: 362.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for remotior_sensus-0.7.7-py3-none-any.whl
Algorithm Hash digest
SHA256 44e23b47f3562d08013be04b699b85a87744950032550ef7c8ce81092d57eef4
MD5 404feaee55c116ce9b66a1775132049f
BLAKE2b-256 a054b3f21c6e1fe7363ef50a13fcffe888382941e57152267028bccbf8d15d94

See more details on using hashes here.

Provenance

The following attestation bundles were made for remotior_sensus-0.7.7-py3-none-any.whl:

Publisher: publish-to-pypi.yml on semiautomaticgit/remotior_sensus

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page