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Compiling OTB Orfeo ToolBox software on Centos/Scientific Linux

The Orfeo ToolBox (OTB), an open-source C++ library for remote sensing images processing, is offering a wealth of algorithms to perform Image manipulation, Data pre-processing, Features extraction, Image Segmentation and Classification, Change detection, Hyperspectral processing, and SAR processing.

Since there is no (fresh) RPM package available for Centos or Scientific Linux, here some quick hints (no full tutorial, though) how to get OTB easily locally compiled. We are following the Installation Chapter.

Importantly, you need to have some libraries installed including GDAL. Be sure that it has been compiled with the  –with-rename-internal-libtiff-symbols and  –with-rename-internal-libgeotiff-symbols flags to avoid namespace collision a.k.a segmentation fault of OTB as per “2.2.4 Building your own qualified Gdal“. We’ll configure and build with the GDAL-internal Tiff and Geotiff libraries that supports BigTiff files

# configure GDAL
./configure \
 --without-libtool \
 --with-geotiff=internal --with-libtiff=internal \
 --with-rename-internal-libtiff-symbols=yes \
 --with-rename-internal-libgeotiff-symbols=yes \
make install

The compilation of the OTB source code requires “cmake” and some other requirements which you can install via “yum install …”. Be sure to have the following structure for compiling OTB, i.e. store the source code in a subdirectory. The binaries will then be compiled in a “build” directory parallel to the OTB-SRC directory:

|-- build/
`-- OTB-SRC/
    |-- Applications/
    |-- CMake/
    |-- CMakeFiles/
    |-- Code/
    |-- Copyright/
    |-- Examples/
    |-- Testing/
    `-- Utilities/

Now it is time to configure everything for OTB. Since I didn’t want to bother with “ccmake”, below the magic lines to compile and install OTB into its own subdirectory within /usr/local/. We’ll use as many internal libraries as possible according to the table in the installation guide. The best way is to save the following lines as a text script “” for easier (re-)use, then run it:


mkdir -p build
cd build

cmake -DCMAKE_INSTALL_PREFIX:PATH=/usr/local/otb-$OTBVER \

make -j4
# note: we assume to have write permission in /usr/local/otb-$OTBVER
make install

That’s it!

In order to use the freshly compiled OTB, be sure to add the new directories for the binaries and the libraries to your PATH and LD_LIBRARY_PATH variables, e.g. in $HOME/.bashrc:

export PATH=$PATH:/usr/local/bin:/usr/local/otb-4.4.0/bin
export LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64/:/usr/local/otb-4.4.0/lib/otb/

Enjoy OTB! And thanks to the OTB developers for making it available.

The post Compiling OTB Orfeo ToolBox software on Centos/Scientific Linux appeared first on GFOSS Blog | GRASS GIS Courses.

New stable release of GRASS GIS 7.0.0!

The GRASS GIS Development team has announced the release of the new major version GRASS GIS 7.0.0. This version provides many new functionalities including spatio-temporal database support, image segmentation, estimation of evapotranspiration and emissivity from satellite imagery, automatic line vertex densification during reprojection, more LIDAR support and a strongly improved graphical user interface experience. GRASS GIS 7.0.0 also offers significantly improved performance for many raster and vector modules: “Many processes that would take hours now take less than a minute, even on my small laptop!” explains Markus Neteler, the coordinator of the development team composed of academics and GIS professionals from around the world. The software is available for Linux, MS-Windows, Mac OSX and other operating systems.

Detailed announcement and software download:

The Geographic Resources Analysis Support System (, commonly referred to as GRASS GIS, is an open source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS 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 presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).

The post New stable release of GRASS GIS 7.0.0! appeared first on GFOSS Blog | GRASS GIS Courses.

Happy 9th Birthday, OSGeo!

Press release by Jeff McKenna, OSGeo Foundation President

9 years ago today was the first ever meeting of the OSGeo foundation, in Chicago U.S.A. (initial press release). Thanks to those passionately involved back then, and the thousands contributing since, now our community has expanded and has reached many countries all over world. Congratulations to everyone for continuing to share the passion for Open Source geospatial.

Here is a glimpse at some of the exciting events happening around the world this year:

The post Happy 9th Birthday, OSGeo! appeared first on GFOSS Blog | GRASS GIS Courses.

GRASS GIS 7: Vector data reprojection with automated vertex densification

GRASS GIS 7 just got better: When reprojecting vector data, now automated vertex densification is applied. This reduces the reprojection error for long lines (or polygon boundaries). The needed improvement has been kindly added in v.proj by Markus Metz.

1. Example

As an (extreme?) example, we generate a box in LatLong/WGS84 (EPSG: 4326) which is of 10 degree side length (see below for screenshot and at bottom for SHAPE file download of this “box” map):

[neteler@oboe ~]$ grass70 ~/grassdata/latlong/grass7/
# for the ease of generating the box, set computational region:
g.region n=60 s=40 w=0 e=30 res=10 -p
projection: 3 (Latitude-Longitude)
zone:       0
datum:      wgs84
ellipsoid:  wgs84
north:      60N
south:      40N
west:       0
east:       30E
nsres:      10
ewres:      10
rows:       2
cols:       3
cells:      6
# generate the box according to current computational region: box_latlong_10deg

Next we start GRASS GIS in a metric projection, here the EU LAEA:

# EPSG 3035, metric EU LAEA:
grass70 ~/grassdata/europe_laea/user1/
GRASS 7.0.0svn (europe_laea): >

Now we first reproject the map the “traditional way” (no vertex densification as in most GIS, here enforced by smax=0):

v.proj box_latlong_10deg out=box_latlong_10deg_no_densification
location=latlong mapset=grass7 smax=0

Then we do a second reprojection with new automated vertex densification (here we use the default values for smax which is a 10km vertex distance in the reprojected map by default):

v.proj box_latlong_10deg out=box_latlong_10deg_yes_densification
location=latlong mapset=grass7

Eventually we can compare both reprojected maps:

g.region vect=box_latlong_10deg_no_densification

# compare:
d.mon wx0
d.vect box_latlong_10deg_no_densification color=red
d.vect box_latlong_10deg_yes_densification color=green fill_color=none
Comparison of the reprojection of a 10 degree large LatLong box to the metric EU LAEA (EPSG 3035): before in red and new in green. The grid is based on WGS84 at 5 degree spacing.

Comparison of the reprojection of a 10 degree large LatLong box to the metric EU LAEA (EPSG 3035): before in red and new in green. The grid is based on WGS84 at 5 degree spacing.

The result shows how nicely the projection is now performed in GRASS GIS 7: the red line output is old, the green color line is the new output (its area filling is not shown).

Consider to benchmark this with other GIS… the reprojected map should not become a simple trapezoid.

2. Sample dataset download

Download of box_latlong_10deg.shp for own tests (1kB).

The post GRASS GIS 7: Vector data reprojection with automated vertex densification appeared first on GFOSS Blog | GRASS GIS Courses.

Landsat 8 captures Trentino in November 2014

The beautiful days in early November 2014 allowed to get some nice views of the Trentino (Northern Italy) – thanks to Landsat 8 and NASA’s open data policy:

Landsat 8: Northern Italy 1 Nov 2014
Landsat 8: Northern Italy 1 Nov 2014

Trento captured by Landsat8
Trento captured by Landsat8

Landsat 8: San Michele - 1 Nov 2014
Landsat 8: San Michele – 1 Nov 2014

The beauty of the landscape but also the human impact (landscape and condensation trails of airplanes) are clearly visible.

All data were processed in GRASS GIS 7 and pansharpened with i.fusion.hpf written by Nikos Alexandris.

The post Landsat 8 captures Trentino in November 2014 appeared first on GFOSS Blog | GRASS GIS Courses.

QGIS 2.6 ‘Brighton’ released

In the new release of QGIS 2.6.0 a series of new features have been added concerning

  • General: new features and bugfixes,
  • DXF export (improvements),
  • Map Composer (enhancements),
  • Processing (including a new modeler implementation),
  • QGIS Server (improvements),
  • Symbology (including user interface improvements),
  • User Interface with improvements.

A visual changelog is available for more details with lots of screenshots.

Congratulations to all QGIS developers! Looking forward to see the Fedora RPM available…

You can download QGIS 2.6 at

The post QGIS 2.6 ‘Brighton’ released appeared first on GFOSS Blog | GRASS GIS Courses.

Selective data removal in an elevation map by means of floodfilling

Do you also sometimes get maps which contain zero (0) rather than NULL (no data) in some parts of the map? This can be easily solved with “floodfilling”, even in a GIS.

My original map looks like this (here, Trentino elevation model):

The light blue parts should be no data (NULL) rather than zero (0)...

Now what? In a paint software we would simply use bucket fill but what about GIS data? Well, we can do something similar using “clumping”. It requires a bit of computational time but works perfectly, even for large DEMs, e.g., all Italy at 20m resolution. Using the open source software GRASS GIS 7, we can compute all “clumps” (that are many for a floating point DEM!):

# first we set the computational region to the raster map:
g.region rast=pat_DTM_2008_derived_2m -p
r.clump pat_DTM_2008_derived_2m out=pat_DTM_2008_derived_2m_clump

The resulting clump map produced by r.clump is nicely colorized:

Clumped map derived from DEM (generated with r.clump)

As we can see, the area of interest (province) is now surrounded by three clumps. With a simple map algebra statement (r.mapcalc or GUI calculator) we can create a MASK by assigning these outer boundary clumps to NULL and the other “good” clumps to 1:

r.mapcalc "no_data_mask = if(pat_DTM_2008_derived_2m_clump == 264485050 || \
   pat_DTM_2008_derived_2m_clump == 197926480 || \
   pat_DTM_2008_derived_2m_clump == 3, null(), 1)"

This mask map looks like this:

Mask map from all clumps except for the large outer clumps

We now activate this MASK and generate a copy of the original map into a new map name by using map algebra again (this just keeps the data matched by the MASK). Eventually we remove the MASK and verify the result:

# apply the mask
r.mask no_data_mask
# generate a copy of the DEM, filter on the fly
r.mapcalc "pat_DTM_2008_derived_2m_fixed = pat_DTM_2008_derived_2m"
# assign a nice color table
r.colors pat_DTM_2008_derived_2m_fixed color=srtmplus
# remove the MASK
r.mask -r

And the final DEM is now properly cleaned up in terms of NULL values (no data):

DEM cleaned up for no data


The post Selective data removal in an elevation map by means of floodfilling appeared first on GFOSS Blog | GRASS GIS Courses.

Rendering a brain CT scan in 3D with GRASS GIS 7

brainscan1Last year (2013) I “enjoyed” a brain CT scan in order to identify a post-surgery issue – luckily nothing found. Being in Italy, like all patients I received a CD-ROM with the scan data on it: so, something to play with! In this article I’ll show how to easily turn 2D scan data into a volumetric (voxel) visualization.

The CT scan data come in a DICOM format which ImageMagick is able to read and convert. Knowing that, we furthermore need the open source software packages GRASS GIS 7 and Paraview to get the job done.

First of all, we create a new XY (unprojected) GRASS location to import the data into:

# create a new, empty location (or use the Location wizard):
grass70 -c ~/grassdata/brain_ct

We now start GRASS GIS 7 with that location. After mounting the CD-ROM we navigate into the image directory therein. The directory name depends on the type of CT scanner which was used in the hospital. The file name suffix may be .IMA.

Now we count the number of images, convert and import them into GRASS GIS:

# list and count
LIST=`ls -1 *.IMA`
MAX=`echo $LIST | wc -w`

# import into XY location:
for i in $LIST ; do

# pretty print the numbers to 000X for easier looping:
curr=`echo $curr | awk ‘{printf “%04d\n”, $1}’`
convert “$i” brain.$curr.png in=brain.$curr.png out=brain.$curr
r.null brain.$curr setnull=0
rm -f brain.$curr.png
curr=`expr $curr + 1`


At this point all CT slices are imported in an ordered way. For extra fun, we can animate the 2D slices in g.gui.animation:

Animation of brain scan slices
(click to enlarge)

# enter in one line:
g.gui.animation rast=`g.mlist -e rast separator=comma pattern=”brain*”`

The tool allows to export as animated GIF or AVI:

Animation of brain scan slices (click to enlarge)

Now it is time to generate a volume:

# first count number of available layers
g.mlist rast pat=”brain*” | wc -l

# now set 3D region to number of available layers (as number of depths)
g.region rast=brain.0003 b=1 t=$MAX -p3

At this point the computational region is properly defined to our 3D raster space. Time to convert the 2D slices into voxels by stacking them on top of each other:

# convert 2D slices to 3D slices: `g.mlist rast pat=”brain*” sep=,` out=brain_vol

We can now look at the volume with GRASS GIS’ wxNVIZ or preferably the extremely powerful Paraview. The latter requires an export of the volume to VTK format:

# fetch some environment variables
eval `g.gisenv -s`
# export GRASS voxels to VTK 3D as 3D points, with scaled z values:
g.message “Exporting to VTK format, scale factor: $SCALE”
r3.out.vtk brain_vol dp=2 elevscale=$SCALE \
output=${PREFIX}_${MAPSET}_brain_vol_scaled${SCALE}.vtk -p

Eventually we can open this new VTK file in Paraview for visual exploration:

# show as volume
# In Paraview: Properties: Apply; Display Repres: volume; etc.
paraview –data=brain_s1_vol_scaled2.vtk

markus_brain_ct_scan3 markus_brain_ct_scan4 markus_brain_ct_scan2













Fairly easy!

BTW: I have a scan of my non-smoker lungs as well :-)

The post Rendering a brain CT scan in 3D with GRASS GIS 7 appeared first on GFOSS Blog | GRASS GIS Courses.

Installing PostGIS on Fedora 20

In order to explore all the new interfaces to PostGIS (from QGIS, GDAL, GRASS GIS 7 and others) I decided to install PostGIS 2.1 on my Fedora 20 Linux box. Eventually it is an easy job but I had to visit a series of blogs to refresh my dark memories from past PostGIS installations done some years ago… So, here the few steps:

# become root
su -
# grab the PostgreSQL 9.3 server and PostGIS 2.1
yum install postgresql-server postgresql-contrib postgis

Now the server is installed but yet inactive and not configured. The next step is to initialize, configure and start the PostgreSQL server:

# initialize DB:
postgresql-setup initdb
# start at boot time:
chkconfig postgresql on
# fire up the daemon:
service postgresql start

A test connection will show that we need to configure TCP/IP connections:

# this will fail
psql -l
psql: could not connect to server: No such file or directory
    Is the server running locally and accepting
    connections on Unix domain socket "/var/run/postgresql/.s.PGSQL.5432"?

So we enable TCP/IP connections listening on port 5432:

# edit "postgresql.conf" with editor of choice (nano, vim, ...),
# add line "listen_addresses = '*'":
vim /var/lib/pgsql/data/postgresql.conf
listen_addresses = '*'
#listen_addresses = 'localhost'         # what IP address(es) to listen on;

Now restart the PostgreSQL daemon:

# restart to re-read configuration
service postgresql restart

Check if the server is now listening on TCP/IP:

# check network connections:
netstat -l | grep postgres
tcp        0      0*               LISTEN     
tcp6       0      0 [::]:postgres           [::]:*                  LISTEN     
unix  2      [ ACC ]     STREAM     LISTENING     2236495  /var/run/postgresql/.s.PGSQL.543

So far so nice. Still PostGIS is not yet active, and we need a database user “gisuser” with password:

# switch from root user to postgres user:
su - postgres
# create new DB user with password (will prompt you for it, choose a strong one):
createuser --pwprompt --encrypted gisuser

Finally we create a first database “gis”:

# create new DB
createdb --encoding=UTF8 --owner=gisuser gis

We enable it for PostGIS 2.1:

# insert PostGIS SQL magic (it should finish with a "COMMIT"):
psql -d gis -f /usr/share/pgsql/contrib/postgis-64.sql

That’s it! Now exit from the “postgres” user account a the “root” account:

# exit from PG user account (back to "root" account level):

In case you want to reach the PostgreSQL/PostGIS server from outside your machine (i.e. from the network), you need to enable PostgreSQL for that:

# enable the network in pg_hba.conf (replace host line; perhaps comment IP6 line):
vim /var/lib/pgsql/data/pg_hba.conf

host    all             all             all                     md5

# save and restart the daemon:
service postgresql restart

Time for another connection test:

# we try our new DB user account on the new database (hostname is the 
# name of the server in the network):
psql --user gisuser -h hostname -l
Password for user gisuser: xxxxxx

Wonderful, we are connected!

# exit from root account:
[neteler@oboe ] $

Now have our PostGIS database ready!

What’s left? Get some spatial data in as a normal user:

# nice tool

Using the shp2pgsql-gui

Next pick a SHAPE file and upload it to PostGIS with “Import”.

Now connect to your PostGIS database with QGIS or GRASS GIS and enjoy!

See also:

The post Installing PostGIS on Fedora 20 appeared first on GFOSS Blog | GRASS GIS Courses.

GDAL/OGR 1.11.0 released

The new version 1.11.0  of GDAL/OGR ( which offers major new features has been released. GDAL/OGR is a C++ geospatial data access library for raster and vector file formats, databases and web services.  It includes bindings for several languages, and a variety of command line tools.


More complete information on the new features and fixes in the 1.11.0 release can be found at

The new release can be downloaded from:

The post GDAL/OGR 1.11.0 released appeared first on GFOSS Blog | GRASS GIS Courses.

Workshop at FOSS4G 2014: Spatio-temporal data handling and visualization in GRASS GIS 7

Drowning in too many maps? Have some fun exploring fascinating geometries of changing landscapes in Space Time Cube and creating 2D and 3D animations from time series of geospatial data. Learn about the new capabilities for spatio-temporal data handling in GRASS GIS 7 ( and explore various techniques for dynamic visualizations.

First, we will introduce you to GRASS GIS 7, including its spatio-temporal capabilities and you will learn how to manage and analyze geospatial data time series. Then, we will explore new tools for visualization of spatio-temporal data. You will create both 2D and 3D dynamic visualizations directly in GRASS GIS 7. Additionally, we will explain the Space Time Cube concept using various applications based on raster and vector data time series. You will learn to manage and visualize data in space time cubes (voxel models). No prior knowledge of GRASS GIS is necessary, we will cover the basics needed for the workshop. All relevant material including an overview of the tools and hands-on practical instructions along with the sample data sets will be available on-line. And, by the way, GRASS GIS is a free and open source geographic information system (GIS) used for geospatial data management, analysis, modeling, image processing, and visualization which runs on Linux, MS Windows, Mac OS X and other systems.

Presenters: Vaclav Petras, Anna Petrasova, Helena Mitasova, Markus Neteler

When:  FOSS4G 2014, Sept 8th-13th 2014, Portland, OR, USA

Register at:

The post Workshop at FOSS4G 2014: Spatio-temporal data handling and visualization in GRASS GIS 7 appeared first on GFOSS Blog | GRASS GIS Courses.

Will the sun shine on us?

Recently, in order to nicely plan ahead for a birthday lunch at the agritur Malga Brigolina (a farm-restaurant near Sopramonte di Trento, Italy, at 1,000m a.s.l. in the Southern Alps), friends of mine asked me the day before:

Will the place be sunny at lunch time for a nice walk?

[well, the weather was close to clear sky conditions but mountains are high here and casting long shadows in the winter time].


A rather easy task I thought, so I got my tools ready since that was an occasion to verify the predictions with some photos! Thanks to the new EU-DEM at 25m I was able to perform the computations right away in a metric system rather than dealing with degree in LatLong.

Direct sunlight can be assessed from the beam radiation map of GRASS GIS’ r.sun when running it for a specific day and time. But even easier, there is a new Addon for GRASS GIS 7 which calculates right away time series of insolation maps given start/stop timestamps and a time step: r.sun.hourly. This shrinks the overall effort to almost nothing.

1. Creating the direct sunlight maps

The first step is to calculate where direct sunlight reaches the ground. Here the input elevation map is the European “eu_dem_25″, while the output is the beam radiation for a certain day (15 Dec 2013 is DOY 349).

Important hint: the computational region must be large enough to east/south/west to capure the cast shadow effects of all relevant surrounding mountains.

I let calculations start at 8am and finish at 5pm which an hourly time step. The authors have kindly parallelized r.sun.hourly, so I let it run on four processors simultaneously to speed up:

# calculate DOY (day-of-year) from given date:
date -d 2013-12-15 +%j

# calculate beam radiation maps for a given time period
# (note: minutes are to be given in decimal time: 30min = 0.5)
r.sun.hourly elev_in=eu_dem_25 beam_rad=trento_beam_doy349 \
      start_time=8 end_time=17 time_step=1 day=349 year=2013 \

The ten resulting maps contain the beam radiation for each pixel considering the cast shadow effects of the pixel-surrounding mountains. However, the question was not to calculate irradiance raster maps in Wh/m^2 but simply “sun-yes” or “sun-no”. So a subsequent filtering had to be applied. Each beam radiation map was filtered: if pixel value equal to 0 then “sun-no”, otherwise “sun-yes” (what my friends wanted to achieve; effectively a conversion into a binary map).
Best done in a simple shell script loop:

[Edit 30 Dec 2013: thanks to Anna you can simplify below loop to the r.colors call thanks to the new -b flag for binary output in r.sun.hourly!]

for map in `g.mlist rast pattern="trento_beam_doy349*"` ; do
    # rename current map to tmp
    g.rename rast=$map,$map.tmp
    # filter and save with original name
    r.mapcalc "$map = if($map.tmp == 0, null(), 1)"
    # colorize the binary map
    echo "1 yellow" | r.colors $map rules=-
    # remove cruft
    g.remove rast=$map.tmp

As a result we got ten binary maps, ideal for using them as overlay with shaded DEMs or OpenStreetMap layers. The areas exposed to direct sunlight are shown in yellow.

Trento direct sunlight 15 Dec 2013 Animation

Trento, direct sunlight, 15 Dec 2013 between 10am and 5pm (See here for creating an animated GIF). Quality reduced for this blog.

2. Time to look at some details: So, is Malga Brigolina in sunlight at lunch time?

Situation at 12pm (noon): predicted that the restaurant is still in shadow – confirmed in the photo:


(click to enlarge)

Situation at 1:30/2:00pm: sun is getting closer to the Malga, as confirmed in photo (note that the left photo is 20min ahead of the map). The small street in the right photo is still in the shadow as predicted in the map):

malga_brigolina_direct_sunlight_15dec2013_14pm_foto(click to enlarge)

Situation at 3:00pm: sun here and there at Malga Brigolina:

malga_brigolina_direct_sunlight_15dec2013_15pm_3DSunlight map blended with OpenStreetmap layer (r.blend + r.composite) and draped over DEM in wxNVIZ of GRASS GIS 7 (click to enlarge). The sunday walk path around Malga Brigolina is the blue/red vector line shown in the view center.

Situation at 4:00pm: we are close to sunset in Trentino… view towards the Rotaliana (Mezzocorona, S. Michele all’Adige), last sunlit summits also seen in photo:

rotaliana_direct_sunlight_15dec2013_16pm_foto(click to enlarge)

3. Outcome

The resulting sunlight/shadow map appear to match nicely realty. Perhaps r.sun.hourly should get an additional flag to generate the binary “sun-yes” – “sun-no” maps directly.

trento_direct_sunlight_15dec2013_15pm_3DDirect sunlight zones (yellow, 15 Dec 2013, 3pm): Trento with Monte Bondone, Paganella, Marzolan, Lago di Caldonazzo, Lago di Levico and surroundings (click to enlarge)

4. GRASS GIS usage note

The wxGUI settings were as simple as this (note the transparency values for the various layers):


Data sources:

The post Will the sun shine on us? appeared first on GFOSS Blog | GRASS GIS Courses.

Video: GRASS GIS development visualization from 1999 to 2013

Watch how the community based GRASS GIS 6.4 software development evolved! You can see how the individual contributors modify and expand the source code:

  • Dec 29, 1999: GRASS GIS 5.0 is being stored in an online source code repository in December 1999…
  • Dec 02, 2000: The developers work on all parts of the code…
  • Jan 15, 2002: Working on the future GRASS GIS 5.1 release
  • Nov 25, 2002: Starting GRASS 5.1 development with code restructuring
  • Jun 14, 2004: GRASS GIS 5.7 released in June 2004
  • Nov 09, 2004: Source code restructuring to get a better directory layout (all other developers waiting…)
  • Nov 09, 2004: … thousands of files are modified in this operation …
  • Nov 10, 2004: All developers resume their activities after the restructuring
  • Jan 10, 2005: Preparing the GRASS GIS 6.0.0 release…
  • Apr 09, 2005: GRASS GIS 6.0.0 published, release branch being split off from trunk for easier maintenance
  • Feb 22, 2006: Release of GRASS GIS 6.0.2 and new source code refactoring startedApr 05, 2006: Heavy development activity in trunk (development branch) …
  • Oct 25, 2006: GRASS GIS 6.2.0 released in October 2006
  • Apr 10, 2007: Preparing the GRASS GIS 6.2.2 release…
  • Jun 16, 2007: GRASS GIS 6.2.2 released in June 2007
  • Nov 01, 2007: Raster and vector modules being actively maintained…
  • Apr 02, 2007: New graphical user interface development speeding up (wxGUI)
  • Feb 20, 2008: Copyright statements prettified in many files
  • May 31, 2008: New GRASS 6 development branch being split off from trunk (which becomes GRASS 7)
  • Jun 10, 2008: Developers moving over to new branch
  • Feb 23, 2009: GRASS 6.4 release branch split off from GRASS 6 development branch
  • Apr 03, 2009: GRASS GIS 6.4 preparations starting…
  • Feb 24, 2010: Intense maintenance in GRASS 6.4 release branch
  • Sep 15, 2010: GRASS GIS 6.4.0 released in September 2010
  • Apr 12, 2011: GRASS GIS 6.4.1 released in April 2011
  • Jun 27, 2011: GRASS GIS 6.4.svn matures for the upcoming 6.4.2 release
  • Aug 16, 2011: Intense maintenance in GRASS 6.4 release branch (GRASS GIS 7 development not shown here)…
  • Feb 19, 2012: GRASS GIS 6.4.2 released in February 2012
  • Nov 13, 2012: Backporting graphical user interface bugfixes from GRASS GIS 7 to GRASS GIS 6.4
  • Apr 17, 2013: Further maintenance in GRASS 6.4 release branch
  • Jul 10, 2013: Fixing odds ‘n ends for the new stable release
  • Jul 27, 2013: GRASS GIS 6.4.3 released in July 2013

The corresponding timeline is also available at


Rendering: Markus Neteler
Audio track editing: Duccio Rocchini & Antonio Galea

Le bruit peut rendre sourd – Track 6/18 Album “Sensation electronique” by Saelynh (CC-BY-NC-ND)

Software used:
Gource software: (GPL)
OpenShot video editor: (GPL

The post Video: GRASS GIS development visualization from 1999 to 2013 appeared first on GFOSS Blog | GRASS GIS Courses.

Using the 25m EU-DEM for shading OpenStreetMap layers

Inspired by Václav Petráš posting about “Did you know that you can see streets of downtown Raleigh in elevation data from NC sample dataset?” I wanted to try the new GRASS GIS 7 Addon r.shaded.pca which creates shades from various directions and combines then into RGB composites just to see what happens when using the new EU-DEM at 25m.

To warm up, I registered the “normally” shaded DEM (previously generated with gdaldem) with r.external in a GRASS GIS 7 location (EPSG 3035, LAEA) and overlayed the OpenStreetMap layer using WMS with GRASS 7′s An easy task thanks to University of Heidelberg’s Indeed, they offer a similar shading via WMS, however, in the screenshot below you see the new EU data being used for controlling the light on our own:

OpenStreetMap shaded with EU DEM 25m

OpenStreetMap shaded with EU DEM 25m (click to enlarge)

Next item: trying r.shaded.pca… It supports multi-core calculation and the possibility to strengthen the effects through z-rescaling. In my example, I used:

r.shaded.pca input=eu_dem_25 output=eu_dem_25_shaded_pca nproc=3 zmult=50

The leads to a colorized hillshading map, again with the OSM data on top (50% transparency):


OpenStreetMap shaded with r.shaded.pca using EU DEM 25m (click to enlarge)

Yes, fun – I like it :-)

Data sources:

The post Using the 25m EU-DEM for shading OpenStreetMap layers appeared first on GFOSS Blog | GRASS GIS Courses.

EU-DEM: new Digital Surface Model at 25m


EU DEM 25m upper Garda Lake area with Riva del Garda and Arco (Italy). 3D view in wxNVIZ – GRASS GIS 7

The 25m European Digital Elevation Model (EU-DEM, Version 1) is a Digital Surface Model (DSM) representing the first surface as illuminated by the sensors:


EU DEM Rotaliana with Mezzocorona and S. Michele (Italy). Produced using Copernicus data and information funded by the European Union – EU-DEM layers.

Its elevations were captured at 1 arc second postings (2.78E-4 degrees). The tiles are provided at 25m resolution in EU-LAEA (EPSG. 3035) projection, temporal coverage: 2000, published in Oct 2013. It is a realisation of the Copernicus programme, managed by the European Commission, DG Enterprise and Industry. Metadata are provided here. According to their “Methodology” page it is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach and it has been generated as a contiguous dataset divided into 1 degree by 1 degree tiles, corresponding to the SRTM naming convention. In addition to the DEM data, a colour shaded relief image over Europe is provided.

From the metadata page: “The EU-DEM data are provided as is, i.e. without a formal validation yet. An independent statistical validation is scheduled as part of the GIO land monitoring service activities, and will be made available in the course of 2014.

1. Data download

Note that the GeoTIFF files are of major size, up to 5 GB:

Hint for GRASS GIS users: instead of importing the data, you can use r.external to simply register the GeoTIFF file within a EU LAEA projected location.


The post EU-DEM: new Digital Surface Model at 25m appeared first on GFOSS Blog | GRASS GIS Courses.

News in GRASS GIS 7

GRASS GIS, commonly referred to as GRASS (Geographic Resources Analysis Support System), is the free Geographic Information System (GIS) software with the longest record of development as FOSS4G community project. The increasing demand for a robust and modern analytical free GIS led to the start of GRASS GIS 7 development in April 2008. Since GRASS 6 more than 10,000 changes have been implemented with a series of new modules for vector network analysis, image processing, voxel analysis, time series management and improved graphical user interface. The core system offers a new Python API and large file support for massive data analysis. Many modules have been undergone major optimization also in terms of speed. The presentation will highlight the advantages for users to migrate to the upcoming GRASS GIS 7 release.

The post News in GRASS GIS 7 appeared first on GFOSS Blog | GRASS GIS Courses.

50th ICA-OSGeo Lab established at Fondazione Edmund Mach (FEM)

We are pleased to announce that the 50th ICA-OSGeo Lab has been established at the GIS and Remote Sensing Unit (Piattaforma GIS & Remote Sensing, PGIS), Research and Innovation Centre (CRI), Fondazione Edmund Mach (FEM), Italy. CRI is a multifaceted research organization established in 2008 under the umbrella of FEM, a private research foundation funded by the government of Autonomous Province of Trento. CRI focuses on studies and innovations in the fields of agriculture, nutrition, and environment, with the aim to generate new sharing knowledge and to contribute to economic growth, social development and the overall improvement of quality of life.

The mission of the PGIS unit is to develop and provide multi-scale approaches for the description of 2-, 3- and 4-dimensional biological systems and processes. Core activities of the unit include acquisition, processing and validation of geo-physical, ecological and spatial datasets collected within various research projects and monitoring activities, along with advanced scientific analysis and data management. These studies involve multi-decadal change analysis of various ecological and physical parameters from continental to landscape level using satellite imagery and other climatic layers. The lab focuses on the geostatistical analysis of such information layers, the creation and processing of indicators, and the production of ecological, landscape genetics, eco-epidemiological and physiological models. The team pursues actively the development of innovative methods and their implementation in a GIS framework including the time series analysis of proximal and remote sensing data.

The GIS and Remote Sensing Unit (PGIS) members strongly support the peer reviewed approach of Free and Open Source software development which is perfectly in line with academic research. PGIS contributes extensively to the open source software development in geospatial (main contributors to GRASS GIS), often collaborating with various other developers and researchers around the globe. In the new ICA-OSGeo lab at FEM international PhD students, university students and trainees are present.

PGIS is focused on knowledge dissemination of open source tools through a series of courses designed for specific user requirement (schools, universities, research institutes), blogs, workshops and conferences. Their recent publication in Trends in Ecology and Evolution underlines the need on using Free and Open Source Software (FOSS) for completely open science. Dr. Markus Neteler, who is leading the group since its formation, has two decades of experience in developing and promoting open source GIS software. Being founding member of the Open Source Geospatial Foundation (, USA), he served on its board of directors from 2006-2011. Luca Delucchi, focal point and responsible person for the new ICA-OSGeo Lab is member of the board of directors of the Associazione Italiana per l’Informazione Geografica Libera (, the Italian Local Chapter of OSGeo). He contributes to several Free and Open Source software and open data projects as developer and trainer.

Details about the GIS and Remote Sensing Unit at

Open Source Geospatial Foundation (OSGeo) is a not-for-profit organisation founded in 2006 whose mission is to support and promote the collaborative development of open source geospatial technologies and data.

International Cartographic Association (ICA) is the world authoritative body for cartography and GIScience. See also the new ICA-OSGeo Labs website.

Processing Landsat 8 data in GRASS GIS 7: RGB composites and pan sharpening


In our first posting (“Processing Landsat 8 data in GRASS GIS 7: Import and visualization“) we imported a Landsat 8 scene (covering Raleigh, NC, USA). In this exercise we use Landsat data converted to reflectance with i.landsat.toar as shown in the first posting.

Here we will try color balancing and pan-sharpening, i.e. applying the higher resolution panchromatic channel to the color channels, using i.landsat.rgb.

1. Landsat 8 – RGB color balancing: natural color composites

After import, the RGB (bands 4,3,2 for Landsat 8) may look initially less exciting than expected.This is easy to fix by a histogram based auto-balancing of the RGB color tables.


To brighten up the RGB composite, we can use the color balancing tool of GRASS GIS 7:


As input, we specify the bands 4, 3, and 2:


Using a “Cropping intensity (upper brightness level)” of 99 (percent), the result look as follows:


For special purposes or under certain atmospheric/ground conditions it may be useful to make use of the functions “Preserve relative colors, adjust brightness only” or “Extend colors to full range of data on each channel” in the “Optional” tab of i.landsat.rgb.


You will need to experiment since the results depend directly on the image data.

2. Landsat 8 pansharpening

Pansharpening is a technique to merge the higher geometrical pixel resolution of the panchromatic band (Band 8) with the lower resolution color bands (Bands 4, 3, 2).

GRASS GIS 7 offers several methods through the command i.pansharpen.

1) Brovey transform:


This module runs in multi-core mode parallelized. The management of the resolution (i.e., apply the higher resolution of the panchromatic band) is performed automatically.


2. IHS transform

Here we select as above the bands in the i.pansharpen interface but use the “ihs” method.


HINT: If the colors should look odd, then apply i.landsat.rgb to the pan-sharpened bands (see above).

Color-adjusted IHS pansharpening (with “Cropping intensity: strength=99″):


Comparison of Landsat 8 RGB composite (39m) and IHS pansharpened RGB composite (15m):

landsat8_rgb432_color_adjusted_zoom landsat8_rgb432_pansharpen_ihs_color_adjusted_zoom

3. PCA transform

Here we select as above the bands in the i.pansharpen interface but use the “pca” method.


Likewise other channels may be merged with i.pansharpen, even when originating from different sensors.

3. Conclusions

Overall, the IHS pansharpening method along with auto-balancing of colors appears to perform very well with Landsat 8.

Compiling QGIS 2.0.1 for Fedora 19 in a few steps

Thanks to Volker Fröhlich’s efforts, a source code RPM package (SRPM) of QGIS 2.0.1 is now available for Fedora. If you are not yet F20 user (like me), you can just take the F20 package and compile it for F19 (or even F18) since there will be no backport of QGIS 2 to F19 (it comes with QGIS 1.8). But: we do want QGIS 2 on Fedora19!

Solution: compile it yourself.

1. 1. Preparations

The best way is  to use “mock” which is used to recompile SRPMS in a separate local environment (“chroot”) without cluttering the system with extra packages needed for the compilation (run as “root”):

yum install mock

2. 2. Get the source code

Next download the SRPM package from the Koji  server:
QGIS: (–> src – download) or check here for more recent versions.

3. 3. Compile it locally as RPM package

The general compilation command (“mock”) would be:

mock -r my_fedora_version_config --rebuild my_source_rpm.src.rpm

So, check for Fedora version config name which is suitable for your system (“my_fedora_version_config“)

ls /etc/mock/

In my case of a 64bit machine, it is “fedora-19-x86_64″. Hence we can compile QGIS 2.0.1 directly from the SRPM file:

mock -r fedora-19-x86_64 --rebuild qgis-2.0.1-2.fc20.src.rpm

Note: the compilation takes 40min on my tiny core i3 laptop (ASUS X202). Use the time to donate some coins to the QGIS project :-)

4. 4. Install and enjoy

Once the compilation job is done, i.e. the binary RPM files are available for installation. To install the freshly compiled QGIS 2.0.1 RPMs, run:

cd /var/lib/mock/fedora-19-x86_64/result/

# an existing QGIS1.8 installation will be replaced: 
yum localinstall qgis-2.0.1-2.fc19.x86_64.rpm \
qgis-grass-2.0.1-2.fc19.x86_64.rpm qgis-python-2.0.1-2.fc19.x86_64.rpm

# consider to cleanup (or keep it for the next update, it is about 1.5GB):
rm -rf /var/lib/mock/fedora-19-x86_64/
# leave the "root" shell

Now we can happily use QGIS 2.0.1 on Fedora 19!



GFOSS DAY 2013 a Bologna: 10-11 Ottobre 2013

Dal 10 al 11 Ottobre 2013 si terrà a Bologna presso i laboratori della Scuola di Ingegneria e Architettura (V.le del Risorgimento, 2), e le sale conferenze della Regione Emilia-Romagna (V.le della Fiera, 8), la sesta conferenza italiana sul software geografico e sui dati geografici liberi (GFOSS DAY 2013).

Lo scopo principale della conferenza è quello di coinvolgere imprese, enti pubblici, scuole, università, centri di ricerca, sviluppatori, cittadini, operatori del settore ed appassionati dei temi del software libero geografico e degli open data.

Sarà inoltre possibile seguire in diretta streaming il convegno.


La partecipazione alla conferenza è libera e gratuita ma è richiesta una registrazione (meglio anticipata) per consentire una migliore organizzazione dell’evento e garantire la stampa di badge e attestati.

L’accesso ai workshop è garantito fino al raggiungimento numero massimo di partecipanti.

Section 1. Programma

Section 2. Sede della conferenza

Parteciperete a #gfoss13? Ditelo al mondo!

Allegato Dimensione
Locandina_GFOSSDAY13.pdf 3.13 MB

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