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 Read more »

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.

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

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.

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. Read more »

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. Read more »

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

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

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. Read more »

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. Read more »

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. Read more »

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. Read more »

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**. Read more »

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.

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 Read more »

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() Read more »

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.