Minor Update: Semi-Automatic Classification Plugin v. 2.1.3


I have updated the Semi-Automatic Classification Plugin for QGIS 2 to version 2.1.3.

This is the changelog:

-bugfix

Very briefly, this is a small bugfix for the classification process, when using SAGA 2.0.8.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.

Minor Update: Semi-Automatic Classification Plugin v. 2.1.2


This post is to inform you about the availability of a new update of the Semi-Automatic Classification Plugin for QGIS 2, which is version 2.1.2.

This is the very brief changelog:

-fix SAGA version according to Processing settings

The Processing framework allows for the selection of the SAGA version installed (i.e. 2.0.8 or 2.1), which is useful especially for Linux users because the installation of SAGA 2.1 could have some dependency issues. This update allows the Semi-Automatic Classification Plugin to work with the SAGA version defined in the Processing settings.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.


QGIS 2 Officially Released


This short post is to inform you that QGIS 2.0.1 "Dofour" has been officially released, and it can be downloaded from here.
I would like to thank all the developers for their valuable work.


Integrate the Python Script that Clips Multiple Rasters at Once in the QGIS Processing Framework (SEXTANTE)


This post is about how to integrate a Python script for clipping rasters in the SEXTANTE toolbox for QGIS (called Processing framework in QGIS 2.0). The script is not run inside the Python Console, but it has a specific interface, and it can be used in the Modeler in order to create automated workflows.
This script allows for the clipping of multiple rasters using a shapefile boundary or the coordinates of a rectangle (similarly to this post). Clipping is useful before processing rasters, in order to limit the classification to the study area.
A video tutorial is at the end of this post.

Interface of the script Clip Multiple Rasters

How to Clip Multiple Rasters at Once Using QGIS, Python and GDAL


An updated tutorial is available here, because this function is included in the Semi-Automatic Classification Plugin. This is a tutorial about how to clip multiple rasters at once in QGIS, running a very simple Python script that uses the command gdal_translate.
This way, every raster loaded in QGIS will be clipped to the same area, defined by a rectangle.
A video tutorial is also available at the end of this post.

Minor Update: Semi-Automatic Classification Plugin v. 2.1.1


I have updated the Semi-Automatic Classification Plugin for QGIS 2 to version 2.1.1.
This is the very brief changelog:

-update to the new Processing framework (SEXTANTE)

The SEXTANTE framework in QGIS 2 has been renamed to Processing. Consequently, all the references to SEXTANTE in my plugin had to be changed.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or downloaded here.

Open Source Software for Geospatial Studies

The OSGeo's Global Conference for Open Source Geospatial Software FOSS4G 2013 will take place from 17th to 21st September, at Nottingham, UK. It is an annual gathering of users and developers that provides an international review of advances in open source software, and fosters the synergy of geospatial projects.

I think it is useful to highlight the connections between the open source programs that allowed for the development of the Semi-Automatic Classification Plugin for QGIS.
I reported these projects in the diagram below. This diagram does not include open source software such as Linux and other operating system utilities, but they are essential for the plugin development.


As you can see, the Semi-Automatic Classification Plugin relies, directly or indirectly, on several other projects; the following are the links to the official websites:
  • QGIS, (of course) a user friendly GIS;
  • GDAL/OGR, a translator library for raster geospatial data formats;
  • GRASS GIS, a GIS software suite used for geospatial data management and analysis;
  • Matplotlib, a python 2D plotting library;
  • Numpy, a package for scientific computing with Python;
  • Orfeo Toolbox, a library of image processing algorithms;
  • Python, a programming language;
  • QT, a cross-platform application and UI framework for developers;
  • SAGA GIS, a GIS for automated geoscientific analyses.

I would like to thank all the developers involved in these projects for their valuable work, and the opportunities that open source software can provide for GIS and remote sensing studies, such as land cover monitoring.

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