Remotior Sensus

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.

Developed by Luca Congedo, Remotior Sensus has the main objective 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. 

Installation Quickstart Tutorials User manual download Source code

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.

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 name alias 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 tool band classification to allow for land cover classification through machine learning. The aim is to simplify the training process and development of the model.

How to cite

Luca Congedo. (2023). semiautomaticgit/remotior_sensus. Zenodo. https://doi.org/10.5281/zenodo.10038132

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/.