Tutorial: Download Sentinel-2 data and calculate the NDVI in Python using Remotior Sensus

This post is about Remotior Sensus, a Python package that allows for the processing of remote sensing images and GIS data.

In this tutorial we'll see how to search and download Sentinel-2 images and calculate the Normalized Difference Vegetation Index (NDVI) using Remotior Sensus.

Following the video of this tutorial.

Tutorial: Create a Sentinel-2 high resolution jpg image Using Remotior Sensus

This is a tutorial about Remotior Sensus, a Python package that allows for the processing of remote sensing images and GIS data.

In particular, this tutorial illustrates how to create a high resolution jpg image from a Sentinel-2 image. Of course, this tutorial could be extended to other satellite images such as Landsat.

Following the video of this tutorial.

Semi-Automatic Classification Plugin major update: version 8.5

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.5.0.

Semi-Automatic Classification Plugin major update: version 8.4.0

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.4.0.

Random Forest Classification of Sentinel-2 image in Python using Remotior Sensus

This video tutorial illustrates how to perform Random Forest classification of a Copernicus Sentinel-2 image using Remotior Sensus, a Python package that allows for the processing of remote sensing images and GIS data.

The tutorial is available as Jupyter notebook in Google Colab, a free service by Google that allows for executing a Jupyter notebook in the cloud.

Following the video of this tutorial.

Tutorial: Random Forest Classification Using the Semi-Automatic Classification Plugin

This is a tutorial about the land cover classification using the Random Forest algorithm in the Semi-Automatic Classification Plugin (SCP).

Please note that the installation of the dependency scikit-learn is required (see Plugin Installation). It is assumed that you have already read the Basic Tutorials.

Following the video tutorial.

Remotior Sensus Video Tutorial: Quickstart

This video tutorial describes the basics of Remotior Sensus, a Python package that allows for the processing of remote sensing images and GIS data.

The tutorial is available as Jupyter notebook in Google Colab, a free service by Google that allows for executing a Jupyter notebook in the cloud.

Following the video of this tutorial.

Partecipate to the Translation of the User Manual and Interface of the Semi-Automatic Classification Plugin

The new version 8.3 of the Semi-Automatic Classification Plugin (SCP) has been recently released, which enables the localization of the user interface.

I invite you to partecipate to the translation of the user manual and the SCP interface to your language. Also, I would like to thank the 190 persons who already joined the translation project.

Your contribution is fundamental for every user who doesn't speak English. It also can be a fun way to learn more about the SCP.

Semi-Automatic Classification Plugin major update: version 8.3.0

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.3.0.

This new version requires Remotior Sensus to be updated to at least version 0.4.0.

During the update process of SCP from version 7 to version 8, several tools were excluded in order to give priority to the main plugin functions.

With this 8.3.0 update, several tools are reintroduced, such as Clustering tool for unsupervised classification (K-means method), the Spectral distance tool, the Edit raster tool, and the Raster zonal stats.

Semi-Automatic Classification Plugin version 8.3 release date

This post is to announce that the new version 8.3 of the Semi-Automatic Classification Plugin (SCP) for QGIS will be released the 3rd of August 2024.

This new version will require the new version 0.4 of the Python processing framework Remotior Sensus, and will include several new features such as such as clustering, raster editing and raster zonal stats.

For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin.

Remotior Sensus Update: Version 0.4

I'm glad to announce the update of Remotior Sensus to version 0.4.

This new version add several new features such as clustering, raster editing and raster zonal stats. Following the complete changelog:

• Added tool "Band clustering" for unsupervised K-means classification of bandset
• Added tool "Raster edit" for direct editing of pixel values based on vector
• Added tool "Raster zonal stats" for calculating statistics of a raster intersecting a vector.
• Improved the NoData handling for multiprocess calculation
• In "Band clip", "Band dilation", "Band erosion", "Band sieve", "Band neighbor", "Band resample" added the option multiple_resolution to keep original resolution of individual rasters, or use the resolution of the first raster for all the bands
• In "Cross classification" fixed area based accuracy and added kappa hat metric
• In "Band combination" added option no_raster_output to avoid the creation of output raster, producing only the table of combinations
• In "Band calc" replaced nanpercentile with optimized calculation function
• Improved extraction of ROIs in "Band classification"
• Minor bug fixing and removed Requests dependency

Tutorial: Using the Semi-Automatic Classification Plugin Interface in the Cloud Through Jupyter and Remotior Sensus

This tutorial illustrates a proof of concept about using Remotior Sensus in Jupyter through an interface similar to the Semi-Automatic Classification Plugin.

Jupyter notebooks are interactive documents that can be edited in a web browser, which allow for coding in Python and interact with widgets. Several cloud services offer Jupyter notebooks to code, and for example Google Colab is a free service by Google that allows for executing a Jupyter notebook in the cloud.

In a recent Remotior Sensus update (v. 0.3), a new module allows for displaying an interface similar to the Semi-Automatic Classification Plugin in Jupyter notebooks, allowing for the interactive search and download of remote sensing images, the interactive creation of Band Sets, training input, and ROIs, and the classification of the bands (the interface is still in development and only a few tools are available).

For example the following command displays the interface for downloading products.

rs.jupyter().download_interface()