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.

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.

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()

Tutorial: Using Remotior Sensus in Copernicus JupyterLab

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 describes the use of Remotior Sensus in Copernicus JupyterLab, which is a Jupyter Notebook service in a web-based environment, offering several tools for working with the Copernicus Data Space.

This service can be accessed at this link https://jupyterhub.dataspace.copernicus.eu after a free registration to the Copernicus Data Space Ecosystem (CDSE).

The Jupyter Notebooks are available in 3 flavors: Small (2 CPU cores and 4GB RAM), Medium (2 CPU cores and 8GB RAM) and Large (4 CPU cores and 16GB RAM). As stated in the documentation, to ensure the fair use of resources by the CDSE users, it is recommended to start with the Small flavor and switch to a bigger only in case of issues with kernel crashing due to the lack of available memory.

Therefore, the Copernicus JupyterLab offers a great opportunity to use Copernicus data in a cloud environment. In this tutorial, we are going to see how to:

• Download and preprocess Sentinel-2 images.
• Create a BandSet and prepare a training input
• Run a Random Forest classification

All the above steps are performed in the cloud. The classification output is saved in a persistent storage with 10GB of space and can be downloaded later.

Tutorial: Random Forest Classification 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 the last few months Remotior Sensus was frequently update to fix and integrate new functions, in particular for the integration with the Semi-Automatic Classification Plugin for QGIS.

In this tutorial we are going to use Remotior Sensus to perform the Random Forest classification of a Copernicus Sentinel-2 image, which involves the following main steps:

1. Create a BandSet using an image
2. Load a training input
3. Perform the random forest classification

Downloading free satellite images using the Semi-Automatic Classification Plugin: the Download product tab

This is part of a series of video tutorials focused on the tools of the Semi-Automatic Classification Plugin (SCP).

In this tutorial, the Download products tab is illustrated, which allows for downloading free satellite images such as Landsat and Sentinel-2.

You can find more information in the user manual at this link.

Following the video tutorial.

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

Managing input bands using the Semi-Automatic Classification Plugin: the Band set tab

This is the first of a series of video tutorials focused on the tools of the Semi-Automatic Classification Plugin (SCP).

In this tutorial, the Band set tab is illustrated, which allows for managing input bands.

You can find more information in the user manual at this link.

Following the video tutorial.

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