Remotior Sensus Update: Version 0.3

I'm glad to announce the update of Remotior Sensus to version 0.3.

The main new feature is a new module for displaying an interface similar to the Semi-Automatic Classification Plugin (which is based on Remotior Sensus) in Jupyter notebooks.

Jupyter notebooks are interactive documents that can be edited in a web browser, which allow for coding in Python and interact with widgets.

The Jupyter interface is still in development and only a few tools are available. For the moment, the available tools are:

• search and download of remote sensing images;
• creation and management of Band Sets;
• the dock for creating a training input and saving ROIs created interactively through polygons or region growing;
• the import of vectors in training input;
• the plot of spectral signatures;
• the classification of the Band Sets;
• a file browser for selecting files or directories.

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

import remotior_sensus
rs = remotior_sensus.Session(n_processes=2, available_ram=1000)
rs.jupyter().download_interface()

Of course this is a proof of concept, considering that this interface doesn't have all the functions of the Semi-Automatic Classification Plugin, and there are a few differences in the look and feel because of the characteristics of Jupyter notebooks.

A tutorial describing this new feature will be released soon.

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

Semi-Automatic Classification Plugin major update: version 8.2.0

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.2.0 which is focused on the download of new products.

This function requires Remotior Sensus to be updated at least to version 0.2.01, which also includes several new products from the Microsoft Planetary Computer. 

Microsoft Planetary Computer is a platform developed by Microsoft to foster environmental sustainability and Earth science through a Data Catalog, a JupyterHub and several other tools.

In this case, Remotior Sensus connects to the Data Catalog to search and download images available from Microsoft Planetary Computer, such as the Landsat archive, MODIS, and Sentinel-2.

Semi-Automatic Classification Plugin update: version 8.1.7

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.1.7 which solves a few bugs and adds the options to download Sentinel-2 images from the Copernicus Data Space Ecosystem.

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.

Call for the Translation of the User Manual and Interface of the Semi-Automatic Classification Plugin

The new version 8 of the Semi-Automatic Classification Plugin (SCP) has been recently released, along with the updated user manual.

I kindly invite you to translate the user manual and the program interface to your language. Your contribution is fundamental for every user who doesn't speak English. It also can be a fun way to deeply learn about the SCP.

Semi-Automatic Classification Plugin update: version 8.1

The Semi-Automatic Classification Plugin (SCP) has been updated to version 8.1 which solves a few bugs and in particular ease the installation of required dependencies.

This update automatically tries to install the required library Remotior Sensus in the plugin directory, if it is not already installed in the QGIS environment, which allows for using the main functions of SCP.

However, it is still recommended to follow the installation instructions to download the required dependencies.

Moreover, an alternative Windows installation using the OSGeo4W network installer has been added to the user manual

https://semiautomaticclassificationmanual.readthedocs.io/en/latest/installation_win64_network.html

Also, the user manual describes the installation in macOS

https://semiautomaticclassificationmanual.readthedocs.io/en/latest/installation_mac.html

and Linux 

https://semiautomaticclassificationmanual.readthedocs.io/en/latest/installation_debian.html

With this update the installation should work in most cases.

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.

Basic Land Cover Classification Using the Semi-Automatic Classification Plugin

This is the first tutorial of the new Semi-Automatic Classification Plugin version 8.

This tutorial describes the essential steps for the classification of a multispectral image (i.e., a modified Copernicus Sentinel-2 image): 

1. Define the Band set and create the Training Input File
2. Create the ROIs
3. Create a Classification Preview
4. Create the Classification Output

Following the video of this tutorial.

The detailed steps of this tutorial are described in the user manual, at the following link

https://semiautomaticclassificationmanual.readthedocs.io/en/latest/tutorial_1.html

I am going to write other tutorials to describe the available classification algorithms, and the other tools of the Semi-Automatic Classification Plugin.

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

Semi-Automatic Classification Plugin version 8 officially released

I am glad to announce the release of the new version 8 (codename "Infinity") of the Semi-Automatic Classification Plugin (SCP) for QGIS.

This new version is based on a completely new Python processing framework that is Remotior Sensus, which expands the processing capabilities of SCP, also allowing for the creation of Python scripts.

The following video provides an introduction to the SCP tools.

Semi-Automatic Classification Plugin version 8 release date and dependency installation

This post is to announce that the new version 8 (codename "Infinity") of the Semi-Automatic Classification Plugin (SCP) for QGIS will be released the 8th of October 2023.

This new version is based on a completely new Python processing framework that is Remotior Sensus, which will expand the processing capabilities of SCP, also allowing for the creation of Python scripts.

The SCP requires Remotior Sensus, GDAL, NumPy and SciPy for most functionalities. Optionally, scikit-learn and PyTorch are required for machine learning. GDAL, NumPy and SciPy should already be installed along with QGIS.

It might be useful to illustrate the installation steps of these dependencies before SCP is released.

Road to the Semi-Automatic Classification Plugin v.8: Landsat and Sentinel-2 images download and preprocessing, classification

This is the second post describing the main new features of the new version 8 (codename "Infinity") of the Semi-Automatic Classification Plugin (SCP) for QGIS, which will be released in October 2023.

The new version is based on Remotior Sensus, a new Python processing framework.

The tool "Download products" has been updated to download Landsat and Sentinel-2 images from different services. In particular, through the service NASA Earthdata (registration required at https://urs.earthdata.nasa.gov) it will be possible to download the Harmonized Landsat and Sentinel-2 which are surface reflectance data product (generated with Landsat 8, Landsat 9, and Sentinel-2 data) with observations every two to three days at 30m spatial resolution (for more information read here). This is therefore a great source for frequent and homogeneous monitoring.

Moreover, Copernicus Sentinel-2 images will be searched through the Copernicus Data Space Ecosystem API, while the images are downloaded through the Google Cloud service that provides the free dataset as part of the Google Public Cloud Data program.

Other download services that were available in SCP 7 (e.g. Sentinel-1, ASTER images) will be available with future updates.

Road to the Semi-Automatic Classification Plugin v.8: Band sets, Band calc and Scripts

As already announced, the new version 8 (codename "Infinity") of the Semi-Automatic Classification Plugin (SCP) for QGIS will be released in October 2023.

This post describes a few main new features of the SCP, which is still under development, based on a completely new Python processing framework that is Remotior Sensus.

The Main interface will include all the tools, as in SCP version 7. The Band set tab will allow to manage more than one Band set; the interface has been restyled with a table on the left to manage the list of Band sets, and the larger table on the right to display the bands of the active band set.

Semi-Automatic Classification Plugin v.8 Release Date

I am glad to announce that the new version of the Semi-Automatic Classification Plugin (SCP) for QGIS will be released in October 2023.

This new version will improve the capabilities of SCP, based on a completely new Python processing framework that is Remotior Sensus, developed for image classification, machine learning and GIS spatial analyses.

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

Remotior Sensus: a Tutorial about Sentinel-2 Download and NDVI Calculation

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

This tutorial describes how to use Remotior Sensus in Google Colab and calculate NDVI from multiple Copernicus Sentinel-2 images. An average NDVI value is computed completely in the cloud, without the need to install software on your local device. 

Remotior Sensus: a Basic Tutorial

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

It describes the main features of Remotior Sensus, such as the management of raster  bands.

Moreover, it includes the download of a Sentinel-2 image, the calculation of NDVI, and a tool to manage tables is presented.   

Also, the user manual is available at https://remotior-sensus.readthedocs.io.

Remotior Sensus: Released the User Manual

The first version of the user manual of Remotior Sensus (a Python package that allows for the processing of remote sensing images and GIS data) has been released.

The user manual is available at https://remotior-sensus.readthedocs.io.

This is of course still in early development, and not all the functions are completely described.

However, most descriptions of the tools are included, also with code examples.

At the moment, the available tools are:

• band calc
• band classification
• band combination
• band dilation
• band erosion
• band mosaic
• band neighbor pixels
• band pca
• band sieve
• cross classification
• download products
• preprocess products
• raster reclassification
• raster report
• raster to vector

Also, the management of raster bands through band sets is available.

Remotior Sensus: Released a new Python package for image classification and GIS spatial analyses

I am very glad to announce the availability of a new Python package that I developed for image classification and GIS spatial analyses:

      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, which 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.

From GIS to Remote Sensing wishes you a Very Happy New Year!

I wish you all a Very Happy New Year!

In 2023 there will be many updates and improvements related to the Semi-Automatic Classification Plugin (SCP) for QGIS 3. 

I'm glad to announce the start of the development of the new SCP version 8 that will be hopefully released in the second half of 2023. 

This update will bring major improvements to the capabilities of SCP, based on a completely new processing framework (more details in a specific post soon) allowing for machine learning and direct use in Python scripts.

I want to thank to all the people who have contributed to the SCP, through their work translating the interface and the user manual, fixing bugs and reporting issues.

Again, very happy new year!