The development team is happy to announce that a new bugfix version of
GRASS GIS has been released today. This release fixes a number of bugs
discovered in the 6.2.2 source code. It is primarily for stability
purposes and adds minimal new features. Besides bug fixes it also
includes a number of new message translations and updates for the help
pages and projection database.
Highlights include further maturation of the GRASS 6 GUI, vector, and
database code. Some improvements have been backported from the GRASS
6.3 development branch where new development continues at a strong
pace of approximately one code commit every hour, including major work
on an all new cross-platform wxPython GUI and a native MS Windows port.
The Geographic Resources Analysis Support System, commonly referred to
as GRASS, is a Geographic Information System (GIS) combining powerful
raster, vector, and geospatial processing engines into a single
integrated software suite. GRASS includes tools for spatial modeling,
visualization of raster and vector data, management and analysis of
geospatial data, and the processing of satellite and aerial
imagery. It also provides the capability to produce sophisticated 4D
presentation graphics and hardcopy maps.
GRASS is currently used around the world in academic and commercial
settings as well as by many governmental agencies and environmental
consulting companies. It runs on a variety of popular hardware
platforms and is Free open-source software released under the terms of
the GNU General Public License.
GRASS is a proposed founding project of the new Open Source Geospatial
Foundation. In support of the movement towards consolidation in the
open source geospatial software world, GRASS is tightly integrated
with the latest GDAL/OGR libraries. This enables access to an
extensive range of raster and vector formats, including OGC-conformal
Simple Features. GRASS also makes use of the highly regarded PROJ.4
software library with support for most known map projections and the
easy definition of new and rare map projections via custom
parameterization. Strong links are maintained with the QuantumGIS and
R Statistics projects with integrated GRASS toolkits available for both.