Semi-Automatic Classification Plugin version 9 officially released

I'm glad to announce the release of the Semi-Automatic Classification Plugin version 9 (codename "Foundation").

 

This new version is compatible with QGIS 4 (based on Qt 6 framework). 

Until QGIS 4 is officially released, in order to try the new Semi-Automatic Classification Plugin you can install the prerelease (QGIS 3.99 master).

 

The following is the changelog:

• new version for QGIS 4
• built on the new Remotior Sensus version 0.6
• new simplified interface designed for new users
• added automatic download of Remotior Sensus if library is not available or outdated
• in the Working toolbar added button to open a Copernicus Browser link at QGIS map coordinates
• in the Working toolbar added buttons to show or hide custom layers or groups by name also using keyboard shortcuts Z, X, and C
• in Download products added option to create band set
• various bug fixing

Remotior Sensus Update: Version 0.6

I'm glad to announce the update of Remotior Sensus to version 0.6.

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

• Added optional dependency Pandas for performance improvement in tabular data.
• In tool “Band classification” added option for using PyTorch pretrained model. In case a pretrained model is selected, and additional algorithm is selected for classification, using the same parameters of the named algorithm (e.g. random forest); after executing the pretrained model, the additional algorithm is executed on the embeddings for classification. Currently, it works with models pretrained by the Allen Institute for Artificial Intelligence (SatlasPretrain: https://satlas-pretrain.allen.ai) in particular, Sentinel-2 swin-v2-base single-image multispectral and swin-v2-tiny single-image multispectral models, and Landsat 8 Landsat 9 swin-v2-base single-image multispectral model. SatlasPretrain model weights are released under the Open Data Commons ‘Attribution License (ODC-BY). The repository code is licensed under the Apache License 2.0 (https://huggingface.co/allenai/satlas-pretrain). This tool downloads the official SatlasPretrain weights (Bastani et al., “SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding”, ICCV 2023, arXiv:2211.15660, https://doi.org/10.48550/arXiv.2211.15660). All model weights remain the property of their respective authors.
• In tool “Band classification” added PyTorch pretreained segmentation models for Sentinel-2 (swin-v2-base single-image multispectral model using 3 bands or 4 bands) pretrained by DPR Team as part of the DPR Zoo Segmentation Hub framework (https://github.com/DPR25/dpr-zoo-segmentation-hub) based on SatlasPretrain models. The model output classes: background, water, developed, tree, shrub, grass, crop, bare, snow, wetland, mangroves, moss. The repository code of DPR Zoo models are licensed under the MIT License (https://huggingface.co/martinkorelic/dpr-zoo-models). This tool downloads the model weights (DPR Team, 2025. Made as part of Arnes Hackathon 2025). All model weights remain the property of their respective authors.
• In tool “Download products” included the download of Sentinel-2 L2A SCL band.
• In tool “Preprocess products” included the Sentinel-2 L2A SCL band.
• Improved progress monitoring for multiprocess.
• Code optimization and bug fixing.
  
  

Many of these enhancements will also be implemented in the Semi-Automatic Classification Plugin for QGIS which will be released on the 20th of February 2026.

 

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

Announcing the development of the Semi-Automatic Classification Plugin version 9

I'm glad to announce that the Semi-Automatic Classification Plugin version 9 (codename "Foundation") is under development.

Information about the USGS Spectral Library Data Download

This post is about recent updates to the USGS Spectral Library Data Download.

Recently the USGS has removed the previous Spectral Library download service and the new download information is at https://www.usgs.gov/media/files/usgs-spectral-library-data-download-information-docx , which describes the download of the whole archive as a .zip file.

Consequently, the Semi-Automatic Classification Plugin tool to Download USGS Spectral Library can't work.

However, it is still possible to import the USGS libraries using the Import tool.

Following the main steps to import a library:

1. download the main archive ASCIIdata_splib07a.zip from https://www.sciencebase.gov/catalog/item/586e8c88e4b0f5ce109fccae
2. create a directory and move one spectral library (name ending with _AREF.txt) and the wavelengths file (file name splib07a_Wavelengths_ASD_0.35-2.5_microns_2151_ch.txt) in it
3. create a .zip file of this directory which can be imported in SCP.

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

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.