GRASS-Wiki - User contributions [en]

Compile and Install Ubuntu

2016-09-28T15:37:03Z

⚠️Marco.minghini: correct package name from "libgsl-ev" to "libgsl-dev"

<div class="boilerplate metadata" id="attention" style="-moz-border-radius:30px; border: 1px double #35824B; margin-bottom: 1.5em; padding: 1em; background-color: #f9f9f9;">
<p style="font-size: 150%; text-align: left;"><span style="color:#35824B">'''Attention'''</span> </p><span style="color:#333333">'''The following instructions describe the compilation and installation of GRASS 6.x or even 7.x and its required dependencies completely from the source on Ubuntu based systems. Please, prefer pre-compiled packages over the manual way described below unless you know ''what'' and ''how'', you want to learn and help testing.'''</span>
</div>

== Very Important notes ==

* Pre-compiled packages and backports are available from [https://wiki.ubuntu.com/UbuntuGIS UbuntuGIS] via their [https://launchpad.net/~ubuntugis/+archive/ppa/ ppa.launchpad] repositories. '''This is by far the simplest and fastest solution. Please prefer it over the manual way described below.'''
** Daily builds of SVN 7.0 release branch and trunk are available from ''ppa:grass/grass-devel''
**: <source lang=bash>sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable</source>
**: <source lang=bash>sudo add-apt-repository ppa:grass/grass-devel</source>
** Latest version of GRASS 7.0 is available from ''ppa:grass/grass-stable''
**: <source lang=bash>sudo add-apt-repository ppa:ubuntugis/ppa</source>
**: <source lang=bash>sudo add-apt-repository ppa:grass/grass-stable</source>
*: <source lang=bash>sudo apt-get update</source>
*: <source lang=bash>sudo apt-get install grass70</source>

* To build an updated version of GRASS or support libraries unmodified, in most cases it will be easier to use an automated build tool such as ''pbuilder'', ''debuild'' or ''cowbuilder''. See the <source lang="bash" enclose="none">debian/</source> directory in the source code for details.

* Some things change fast and therefore it is expected that the instructions might fail. Please inform the grass-user list in case something just does not work (like for example non-available dependecies/packages from the repositories) or update this page respectively.

* GRASS version 6.5 exists for development purposes, testing features to-be backported to version 6.4. As such it may include unstable code and is not intended for production and end-users.

* An alternate method is to use the DebianGIS packaging scripts, which enable a lot of this to happen automatically (see [https://svn.osgeo.org/grass/grass/branches/releasebranch_6_4/debian/README.debian debian/README.debian] in the GRASS Subversion source code repository). Specifically, this command will get you 90% of the way there: <source lang="bash">sudo apt-get build-dep grass</source>
: ''(you'll need to have the deb-src lines active in your /etc/apt/sources.list file)''

== Quick instructions ==

<source lang="bash">
tar xzf grass-7.0.4.tar.gz
cd grass-7.0.4/

# install build dependency packages:
sudo apt-get build-dep grass

# configure to taste..
CFLAGS="-O2 -Wall" LDFLAGS="-s" ./configure \
--enable-largefile=yes \
--with-nls \
--with-cxx \
--with-proj-share=/usr/share/proj/ \
--with-geos \
--with-wxwidgets \
--with-cairo \
--with-opengl-libs=/usr/include/GL \
--with-freetype=yes --with-freetype-includes="/usr/include/freetype2/" \
--with-postgres=yes --with-postgres-includes="/usr/include/postgresql" \
--with-sqlite=yes \
--with-mysql=yes --with-mysql-includes="/usr/include/mysql" \
--with-odbc=no \
2>&1 | tee config_log.txt

# build using 4 CPU cores
time make -j 4 2>&1 | tee build_log.txt

sudo make install
</source>

== Hints ==

* Usually, the installation of compiled code is done by using the <source lang="bash" enclose="none">make</source> tool. Alternatively, this can be done by using the ''checkinstall'' tool (i.e., <source lang="bash" enclose="none">sudo checkinstall</source> instead of <source lang="bash" enclose="none">sudo make install</source>) which eases off removal of packages. If ''checkinstall'' fails to deliver, please note some related bugs: [https://bugs.launchpad.net/ubuntu/+source/checkinstall/+bug/78455 78455] and [https://bugs.launchpad.net/ubuntu/+source/checkinstall/+bug/599163 599163]. Useful information on using ''checkinstall'': [https://help.ubuntu.com/community/CompilingEasyHowTo Compiling things on Ubuntu the Easy Way].

* In multi-core processors, the compilation performance can be boosted by using ''-j'' switches (e.g. <source lang="bash" enclose="none">make -j2</source> or <source lang="bash" enclose="none">make -j3</source> or even <source lang="bash" enclose="none">make -j4</source>) which specify the number of jobs (commands) to run simultaneously.

== Dependencies ==

=== Current stable Ubuntu version ===
First, update the system from the repositories<source lang="bash">
sudo apt-get update && sudo apt-get upgrade
</source>

Then, install ''SQLite'', ''SVN'' and ''dependencies'' for compiling PROJ, GEOS, GDAL/OGR, GRASS, GDAL-GRASS-PLUGIN (some additional packages may be required in this case); the following action will also install various dependencies (listed in the command line as <source lang="bash" enclose="none">The following extra packages will be installed:</source>...):<br/>

The following dependencies concern [http://releases.ubuntu.com/xenial/ Ubuntu Xenial Xerus (16.04 LTS)]

<source lang="bash">
# this is a single command, please copy-paste it entirely into the terminal:
sudo apt-get install \
build-essential \
flex make bison gcc libgcc1 g++ cmake ccache \
python python-dev \
python-opengl \
python-wxversion python-wxtools python-wxgtk3.0 \
python-dateutil libgsl-dev python-numpy \
wx3.0-headers wx-common libwxgtk3.0-dev \
libwxbase3.0-dev \
libncurses5-dev \
zlib1g-dev gettext \
libtiff5-dev libpnglite-dev \
libcairo2 libcairo2-dev \
sqlite3 libsqlite3-dev \
libpq-dev \
libreadline6 libreadline6-dev libfreetype6-dev \
libfftw3-3 libfftw3-dev \
libboost-thread-dev libboost-program-options-dev liblas-c-dev \
resolvconf \
libjasper-dev \
subversion \
libav-tools libavutil-dev ffmpeg2theora \
libffmpegthumbnailer-dev \
libavcodec-dev \
libxmu-dev \
libavformat-dev libswscale-dev \
checkinstall \
libglu1-mesa-dev libxmu-dev \
ghostscript
</source>

The following dependencies concern [http://releases.ubuntu.com/trusty/ Ubuntu Trusty Tahr (14.04 LTS)]

<source lang="bash">
# this is a single command, please copy-paste it entirely into the terminal:
sudo apt-get install \
build-essential \
flex make bison gcc libgcc1 g++ cmake ccache \
python python-dev \
python-opengl \
python-wxversion python-wxtools python-wxgtk2.8 \
python-dateutil libgsl0-dev python-numpy \
wx2.8-headers wx-common libwxgtk2.8-dev libwxgtk2.8-dbg \
libwxbase2.8-dev libwxbase2.8-dbg \
libncurses5-dev \
zlib1g-dev gettext \
libtiff-dev libpnglite-dev \
libcairo2 libcairo2-dev \
sqlite3 libsqlite3-dev \
libpq-dev \
libreadline6 libreadline6-dev libfreetype6-dev \
libfftw3-3 libfftw3-dev \
libboost-thread-dev libboost-program-options-dev liblas-c-dev \
resolvconf \
libjasper-dev \
subversion \
libav-tools libavutil-dev ffmpeg2theora \
libffmpegthumbnailer-dev \
libavcodec-dev \
libxmu-dev \
libavformat-dev libswscale-dev \
checkinstall \
libglu1-mesa-dev libxmu-dev \
ghostscript</source>

* See the additional packages bellow and in other sections and install them. Note that some of them are [http://grass.osgeo.org/grass70/source/REQUIREMENTS.html required], namely PROJ.4, GEOS and GDAL. If you don't have special requirements, it is usually enough just to install PROJ.4, GEOS and GDAL from repository (rather then compile them manually).

* for mysql support, <source lang="bash" enclose="none">libmysqlclient-dev</source> is required <source lang="bash">
sudo apt-get install libmysqlclient-dev
</source>

* for netcdf support, <source lang="bash" enclose="none">netcdf-bin</source> and <source lang="bash" enclose="none">libnetcdf-dev</source> is required <source lang="bash">
sudo apt-get install netcdf-bin libnetcdf-dev
</source>

* create a directory as a simple user where all source code is going to be stored -- in this example, a directory named <source lang="bash" enclose="none">src</source> under <source lang="bash" enclose="none">/usr/local</source> is created <source lang="bash">
sudo mkdir /usr/local/src
</source>

* take over directories ownerships ('''replace''' below the terms ''userid'' and ''groupid'' with a real <source lang="bash" enclose="none">userid</source>): <source lang="bash">
sudo chown userid:groupid /usr/local/src</source>

* similarly, grant <source lang="bash" enclose="none">rwx</source> (read-write-execute) permissions for our ''userid'' and ''groupid'' onto the <source lang="bash" enclose="none">src</source> directory: <source lang="bash">
sudo chmod ug+rwx /usr/local/src</source>

=== Earlier Ubuntu versions ===

For [http://old-releases.ubuntu.com/releases/ earlier Ubuntu versions], '''watch out for dependency differences!''' Modify the dependency list given above as instructed below.

* for [http://releases.ubuntu.com/raring/ Ubuntu Raring Ringtail (13.04]), change the following dependencies:
** <source lang="bash" enclose="none">libav-tools libavutil-dev --> ffmpeg</source>
** <source lang="bash" enclose="none"> --> lesstif2-dev</source>

* for [http://releases.ubuntu.com/precise/ Ubuntu Precise Pangolin (12.04)], change the following dependencies:
** <source lang="bash" enclose="none">libpnglite-dev --> libpngwriter-dev</source>
** <source lang="bash" enclose="none">libtiff5-dev --> libtiff4-dev</source>

* for [http://old-releases.ubuntu.com/releases/maverick/ Ubuntu Maverick Meerkat (10.10)] or later, change the following dependencies:
** <source lang="bash" enclose="none">libpngwriter-dev --> libpngwriter0-dev</source>
** <source lang="bash" enclose="none">libcairo-dev --> libcairo2-dev</source>
** <source lang="bash" enclose="none">fftw3 --> libfftw3-3</source>
** <source lang="bash" enclose="none">fftw3-dev --> libfftw3-dev</source>

* for [http://old-releases.ubuntu.com/releases/lucid/ Ubuntu Lucid Lynx (10.04)] or later, also install: <source lang="bash">sudo apt-get install libhdf4-alt-dev libhdf4-0-alt</source>

* for [http://old-releases.ubuntu.com/releases/ earlier Ubuntu versions], also install: <source lang="bash"> sudo apt-get install libhdf4g-dev libhdf4g-run</source>

== Using pre-compiled dev Packages for PROJ.4, GEOS and GDAL ==

=== PROJ.4 ===

Install the <source lang="bash" enclose="none">dev</source> package:

<source lang="bash">
sudo apt-get install libproj-dev proj-data proj-bin
</source>

* In the call to <source lang="bash" enclose="none">./configure</source> for [[#GRASS|GRASS]], replace <source lang="bash" enclose="none">--with-proj-share=/usr/local/share/proj/</source> by <source lang="bash" enclose="none">--with-proj-share=/usr/share/proj/</source>

=== GEOS ===

Install the <source lang="bash" enclose="none">dev</source> package:

<source lang="bash">
# probably you also need to additionally install "libgeos-c1v5"
sudo apt-get install libgeos-dev
</source>

* In the call to <source lang="bash" enclose="none">./configure</source> for [[#GRASS|GRASS]], replace <source lang="bash" enclose="none">--with-geos=/usr/local/bin/geos-config</source> by <source lang="bash" enclose="none">--with-geos=/usr/bin/geos-config</source>

=== GDAL ===

Install the <source lang="bash" enclose="none">dev</source> package (possibly without support for datumgrid):

<source lang="bash">
sudo apt-get install libgdal-dev
</source>

For support of WMS in wxGUI install Python GDAL bindings and GDAL executables:
<source lang="bash">
sudo apt-get install python-gdal gdal-bin
</source>

* Install also the required extra packages (note the message: <source lang="bash" enclose="none">The following extra packages will be installed:</source>)
* Look out for packages to be removed by this operation -- this is most likely caused by incompatible package versions. Fix these problems in advance using commands like the following: <source lang="bash">
sudo apt-get install <package>=<required.version></source>

=== GRASS-GIS ===

[[Compile_and_Install_Ubuntu#GRASS_GIS|Jump to sub-section GRASS-GIS below]]

== Using pre-compiled dev Packages for PROJ.4, GEOS and GDAL from GIS.lab PPA==

Ivan Mincik has made all required packages available in his [https://launchpad.net/~imincik/+archive/ubuntu/gis PPA]:

<source lang="bash">
sudo add-apt-repository ppa:imincik/gis
sudo apt-get install libproj-dev libgdal-dev python-gdal libgeos-dev
</source>

Now you can either also install GRASS GIS 7 from there or compile it yourself (see [[Compile_and_Install_Ubuntu#GRASS_GIS|Jump to sub-section GRASS-GIS below]])

== Compile from source ==

=== PROJ4 ===

* within the directory <source lang="bash" enclose="none">/usr/local/src</source> (create it if it does not exist) checkout <source lang="bash" enclose="none">proj</source> from its Subversion repository: <source lang="bash">
svn co http://svn.osgeo.org/metacrs/proj/branches/4.8/proj/
</source>

* get [http://download.osgeo.org/proj/proj-datumgrid-1.5.zip '''proj-datumgrid-1.5.zip'''] from [http://trac.osgeo.org/proj proj' trac] and move it under <source lang="bash" enclose="none">proj/nad</source>
<ul><source lang="bash">
wget http://download.osgeo.org/proj/proj-datumgrid-1.5.zip
mv proj-datumgrid-1.5.zip /usr/local/src/proj/nad
cd /usr/local/src/proj/nad
</source>
</ul>

* decompress it <source lang="bash">
unzip proj-datumgrid-1.5.zip
</source>

* go back to the <source lang="bash" enclose="none">proj</source> directory <source lang="bash">
cd /usr/local/src/proj
</source>

* if required, clean previous configuration & compilation <source lang="bash">
make distclean
</source>

* simple configure, compile and install <source lang="bash">
./configure && make && sudo make install
</source>
<ul> or <source lang="bash">
./configure && make -j2 && sudo checkinstall
</source>
</ul>

* ensure that <source lang="bash" enclose="none">/usr/local/lib</source> is added to <source lang="bash" enclose="none">/etc/ld.so.conf</source> and afterwards run <source lang="bash">
sudo ldconfig
</source>

* finally, go back to the parent directory simply by instructing<source lang="bash">
cd ..
</source>

=== GEOS ===

* download '''geos-3.4.2.tar.bz2''' from [http://trac.osgeo.org/geos/ http://trac.osgeo.org/geos] using <source lang="bash" enclose="none">wget</source> <source lang="bash">
wget http://download.osgeo.org/geos/geos-3.4.2.tar.bz2
</source>

* move to the "source-code" directory and decompress
<ul><source lang="bash">
cd /usr/local/src/
bunzip2 geos-3.4.2.tar.bz2
tar xvf geos-3.4.2.tar
</source>
</ul>

* if required, clean previous configuration & compilation <source lang="bash">
make distclean
</source>

* move to geos directory <source lang="bash">
cd geos-3.4.2
</source>

* simple configure, compile and install <source lang="bash">
./configure && make && sudo make install
</source>
<ul> or <source lang="bash">
./configure && make -j2 && sudo checkinstall
</source>
</ul>

* do not forget to execute <source lang="bash">
sudo ldconfig
</source>

=== GDAL ===

'''Note''', GDAL must be compiled '''without''' GRASS support

* download the current stable version <source lang="bash">
svn co https://svn.osgeo.org/gdal/branches/1.11/gdal gdal_stable
</source>

* enter in the <source lang="bash" enclose="none">gdal_stable</source> directory <source lang="bash">
cd /usr/local/src/gdal_stable
</source>

* optionally, update the source code <source lang="bash">
svn up
</source>

* if required, clean previous configurations/compilations <source lang="bash">
make distclean
</source>

* a simple configuration without any parameters will detect and support various installed libraries <source lang="bash">
./configure
</source>

* skip to the ''compile and install'' step or check the following customised configuration example
<ul>
<source lang="bash">
CFLAGS="-g -Wall" LDFLAGS="-s" ./configure \
--with-png=internal \
--with-libtiff=internal \
--with-geotiff=internal \
--with-jpeg=internal \
--with-gif=internal \
--with-ecw=no \
--with-expat=yes \
--with-sqlite3=yes \
--with-geos=yes \
--with-python \
--with-libz=internal \
--with-netcdf \
--with-threads=yes \
--without-grass \
--without-ogdi \
--with-pg=/usr/bin/pg_config \
--with-xerces=yes
</source>
</ul>

* compile, install & ldconfig <source lang="bash">
make -j2 && sudo make install && sudo ldconfig
</source>
<ul> or <source lang="bash">
make -j2 && sudo checkinstall && sudo ldconfig
</source>
</ul>

=== GRASS GIS ===

Note the differences between different GRASS version (SVN branches) in download and cofiguration. Note also the changes required if you installed some of the dependencies from packages (rather then compiled them yourself).

To fully understand the build process, read the <code>INSTALL</code> file, which is located in GRASS' source code root directory. For example, if you have problems related to 32bit versus 64bit, pay attention to section <code>(C)</code>, entitled <code>COMPILATION NOTES for 64bit platforms</code>.

''' Getting GRASS' source code '''

Select from one of the GRASS GIS versions and download (using SVN) the source code:

* OLD STABLE VERSION: current state of the 6.4.x release branch version (stable) <source lang="bash">
svn co https://svn.osgeo.org/grass/grass/branches/releasebranch_6_4 grass64_release
</source>

* STABLE VERSION: current state of the 7.0.x release branch version (future stable) <source lang="bash">
svn co https://svn.osgeo.org/grass/grass/branches/releasebranch_7_0 grass70_release
</source>

* DEVELOPMENT VERSION: current state of the trunk (latest version of code where the development happens) <source lang="bash">
svn co https://svn.osgeo.org/grass/grass/trunk grass7_trunk
</source>

* for other versions see [http://trac.osgeo.org/grass/wiki/DownloadSource GRASS Trac wiki].

''' Configure, Compile and Install'''

Enter the directory with the source source code (create by svn), for example:

<source lang="bash">
cd grass70_release
</source>

* '''GRASS GIS 6 example configuration''' (which can/should be adjusted according to specific needs):
<ul>
<source lang="bash">
CFLAGS="-O2 -Wall" LDFLAGS="-s" ./configure \
--enable-largefile=yes \
--with-nls \
--with-cxx \
--with-proj-share=/usr/local/share/proj/ \
--with-geos=/usr/local/bin/geos-config \
--with-readline \
--with-python=yes \
--with-wxwidgets \
--with-cairo \
--with-opengl-libs=/usr/include/GL \
--with-motif \
--with-tcltk-includes="/usr/include/tcl8.5" \
--with-ffmpeg=yes --with-ffmpeg-includes="/usr/include/libavcodec /usr/include/libavformat /usr/include/libswscale /usr/include/libavutil" \
--with-freetype=yes --with-freetype-includes="/usr/include/freetype2/" \
--with-postgres=yes --with-postgres-includes="/usr/include/postgresql" \
--with-sqlite=yes \
--with-mysql=yes --with-mysql-includes="/usr/include/mysql" \
--with-odbc=no
</source>
</ul>
<ul>
'''Note''', the above configuration uses the <code>Proj4</code> and <code>GEOS</code> packages compiled from the source. In the case that pre-compiled versions from the repository are required, remove the above corresponding lines to use the "defaults", i.e. <source lang="bash">
--with-proj-share=/usr/share/proj \
--with-geos=/usr/bin/geos-config \
</source>
</ul>

* '''GRASS GIS 7 example configuration''' (which can/should be adjusted according to specific needs):
<ul>
<source lang="bash">
CFLAGS="-O2 -Wall" LDFLAGS="-s" ./configure \
--enable-largefile=yes \
--with-nls \
--with-cxx \
--with-readline \
--with-pthread \
--with-proj-share=/usr/local/share/proj/ \
--with-geos=/usr/local/bin/geos-config \
--with-wxwidgets \
--with-cairo \
--with-opengl-libs=/usr/include/GL \
--with-freetype=yes --with-freetype-includes="/usr/include/freetype2/" \
--with-postgres=yes --with-postgres-includes="/usr/include/postgresql" \
--with-sqlite=yes \
--with-mysql=yes --with-mysql-includes="/usr/include/mysql" \
--with-odbc=no \
--with-liblas=yes --with-liblas-config=/usr/bin/liblas-config
</source>
</ul>
<ul>
'''Note''', the above configuration uses the <code>Proj4</code> and <code>GEOS</code> packages compiled from the source. In the case that pre-compiled versions from the repository are required, remove the above corresponding lines to use the "defaults", i.e. (note the backslashe at the end of each line)
<source lang="bash">
--with-proj-share=/usr/share/proj \
--with-geos=/usr/bin/geos-config \
</source>
'''Note 2''', if compiling with liblas, you will want liblas compiled with laszip support. liblas will look for laszip includes in /usr/local/include/laszip by default. Creating the laszip directory in /usr/local/include and making a soft link. ln -s /usr/local/include/lasz*.hpp /usr/local/include/laszip and ln -s /usr/local/include/lasunz*.hpp /usr/local/include/laszip should allow liblas to compile with laszip support
</ul>

* compile & install <source lang="bash">
make -j2 && sudo make install && sudo ldconfig
</source>

<ul> or <source lang="bash">
make -j2 && sudo checkinstall && sudo ldconfig
</source>
</ul>

For subsequent updates execute (not need for the first time):
<source lang="bash">
svn up
make -j2 && sudo make install && sudo ldconfig
</source>

Sometimes, it is required to clean previous configuration and compilation:
<source lang="bash">
make distclean
svn up
./configure ... # (use the configure command above)
make -j2 && sudo make install && sudo ldconfig
</source>

=== GDAL-GRASS-Plugin ===

* get the plugin from [http://download.osgeo.org/gdal OSGeo's Download Server]: [http://download.osgeo.org/gdal/gdal-grass-1.4.3.tar.gz http://download.osgeo.org/gdal/gdal-grass-1.4.3.tar.gz] using <source lang="bash" enclose="none">wget</source>
<ul><source lang="bash">
wget http://download.osgeo.org/gdal/gdal-grass-1.4.3.tar.gz
tar xvzf gdal-grass-1.4.3.tar.gz
cd gdal-grass-1.4.3
</source></ul>

* create <source lang="bash" enclose="none">/etc/ld.so.conf.d/grass.conf</source> or add in <source lang="bash" enclose="none">/etc/ld.so.conf</source> the GRASS library path:
<ul><source lang="bash">
# GRASS 6.4 library path
/usr/local/src/grass64_release/lib

# GRASS 6.5 library path
/usr/local/src/grass6_devel/lib

# GRASS 7.0 library path
/usr/local/src/grass64_release/lib

# GRASS 7 (development version) library path
/usr/local/src/grass7_trunk/lib
</source></ul>

* optionally, clean previous configurations/compilations<source lang="bash">
make distclean</source>

* configure -- point to GRASS installation/binaries
<ul><source lang="bash"> ./configure \
--prefix=/usr/local \
--with-gdal=/usr/local/bin/gdal-config \
--with-grass=/usr/local/grass-6.4.4svn/ \
--with-autoload="/usr/local/lib/gdalplugins/" \
--with-ld-shared="g++ -shared"</source></ul>
<ul>
* for GRASS 6.5, replace the respective line above, depending on where the source code in question is stored, with something like <source lang="bash" enclose="none"> --with-grass=/usr/local/grass-6.5.svn/</source>
* for GRASS 7.0, replace with <source lang="bash" enclose="none"> --with-grass=/usr/local/grass-7.0.0svn/</source>
* for GRASS 7, replace with <source lang="bash" enclose="none"> --with-grass=/usr/local/grass_trunk/</source>
</ul>

* compile & install using checkinstall<source lang="bash">
make -j2 && sudo checkinstall</source>

= Post compilation/installation control =

* For a recommended quick-check read the [http://grass.osgeo.org/wiki/Compile_and_install_GRASS_and_QGIS_with_GDAL/OGR_Plugin#Troubleshooting Troubleshooting] section at [http://grass.osgeo.org/wiki/Compile_and_install_GDAL-GRASS_plugin Compile_and_install_GDAL-GRASS_plugin]

* in case of errors in future compilation attempts, remember to remove program binaries and files created with the "configuration" from previous compilations with
make distclean

* another common mistake is compiling a module against some GRASS version and then try to run it through another GRASS version. The solution is to recompile the affected module or, in case there are multiple GRASS installations, set up properly LD_LIBRARY_PATH paths.

= Removal of GRASS =

To get rid of a GRASS binaries installation, delete
* <source lang="bash" enclose="none">/usr/local/grass-6.4.4svn</source> (directory, binaries location)
* <source lang="bash" enclose="none">/usr/local/bin/grass64</source> (file)
* <source lang="bash" enclose="none">/usr/local/bin/gem64</source> (file)
* <source lang="bash" enclose="none">/home/username/.grassrc6</source> (file)

If wanted, delete also the complete source code:
* <source lang="bash" enclose="none">/usr/local/src/grass64_release</source> (directory, source code location)

To remove <code>grass</code> (or any other package) which was installed by <source lang="bash" enclose="none">checkinstall</source>, use <source lang="bash" enclose="none">dpkg</source>, e.g.<source lang="bash">
sudo dpkg -r grass64 # package name defined at installation is important</source>

= Packaging of GRASS =

* See the {{src|debian/README.debian}} file in the GRASS source code for directions on rolling your own packages.

For details about the auto-nightly-builds and after-market supplied packages from UbuntuGIS, please refer to the [[Ubuntu Packaging]] wiki page.

== See also ==

* GRASS GIS in Ubuntu installation: http://www.spatial-ecology.net/dokuwiki/doku.php?id=wiki:install_linux
* Docker: https://registry.hub.docker.com/u/javimarlop/ubuntugis-docker/dockerfile/
* [[Ubuntu Packaging]]
* [[GRASS in Debian]]

= Archived Notes =

Notes that concern the configuration and compilation of GRASS GIS and its dependencies for older versions of Ubuntu-Linux.

== Ubuntu 7.10 64-bit ==

* Compiling latest GRASS source code on a 64-bit machine (with an ATI graphic card) under Ubuntu 7.10 64-bit with support for: 64-bit, SQLite, OpenGL, PYTHON, FFMPEG
(Based on "Ubuntu 6.06 LTS - GRASS 6.1 Compilation Script" by David Finlayson)
''Assuming it is the first time attempting to compile GRASS' source code & installing SVN, PROJ, GDAL/OGR''

'''Preparation'''
sudo apt-get update && sudo apt-get upgrade

* install dependencies for compiling (in general) and dependencies for GRASS: PROJ, GDAL/OGR
sudo apt-get install grass build-essential flex bison libncurses5-dev zlib1g-dev \
libgdal1-dev libtiff4-dev libgcc1 libpng12-dev tcl8.4-dev tk8.4-dev fftw3-dev \
libfreetype6-dev libavcodec-dev libxmu-dev gdal-bin libreadline5 libreadline5-dev \
make python-dev python-wxversion

* install SQLite
sudo apt-get install sqlite3 libsqlite3-dev

* install SVN
sudo apt-get install subversion

* create a directory as a simple user where source code(s) are going to be stored (in our example we use a directory called '''src''' under '''/usr/local''')

sudo mkdir /usr/local/src

* grant rwx (read-write-execute) permissions for our userid/ groupid on the directory (replace words userid and groupid with real userid):
sudo chown ''userid'':''groupid'' /usr/local/src

sudo chmod ug+rwx /usr/local/src

* download latest source code from GRASS SVN repository in a directory on the system (e.g. /usr/local/src)
svn checkout https://svn.osgeo.org/grass/grass/trunk grass_trunk

* Above command places GRASS' source code in '''/usr/local/src/grass_trunk'''. In case of a subsequent update use the command: '''svn up''' from within the grass_trunk directory

----
'''Before''' attempting to compile GRASS, READ section (C) in the '''INSTALL''' file located in the main directory of GRASS source code entitled:
'''(C) COMPILATION NOTES for 64bit platforms'''
----

* installing FFTW3 if not already on system
sudo apt-get install fftw3 fftw3-dev

'''FFMPEG'''

Note: Back in Ubuntu 7.10, installing ffmpeg through the repositories wouldn't work with grass. The following steps were successfully used.

* install FFMPEG (information taken from: http://stream0.org/2008/01/install-ffmpeg-on-ubuntu-gutsy.html)
* download source code with svn
svn checkout svn://svn.mplayerhq.hu/ffmpeg/trunk ffmpeg

* install dependencies
sudo apt-get install liblame-dev libfaad2-dev libfaac-dev libxvidcore4-dev \
liba52-0.7.4 liba52-0.7.4-dev libx264-dev libdts-dev checkinstall \
build-essential subversion

* guide to ffmpeg directory
cd ffmpeg

if necessary: '''make distclean''' before configuration (look at notes below)

* configuration ('''note:''' the configuration parameter "'''--enable-pp'''" does not work anymore)
# configure FFMPEG
./configure --enable-gpl --enable-libvorbis --enable-libtheora \
--enable-liba52 --enable-libdc1394 --enable-libgsm \
--enable-libmp3lame --enable-libfaad --enable-libfaac \
--enable-libxvid --enable-pthreads --enable-libx264 \
--enable-shared

* compilation
make

* installation on /usr/local/bin -- important to remember when configuring GRASS' source code for compilation
sudo checkinstall

'''Go for GRASS!'''
* in our example we used the /usr/local/src directory to store GRASS' source code, so:
cd /usr/local/src/grass_trunk

* configuration
CFLAGS="-g -Wall" ./configure --enable-64bit \
--with-libs=/usr/lib64 --with-cxx --with-freetype=yes \
--with-postgres=no --with-sqlite=yes --enable-largefile=yes \
--with-tcltk-includes=/usr/include/tcl8.4 \
--with-freetype-includes=/usr/include/freetype2 \
--with-opengl-libs=/usr/include/GL --with-readline \
--with-python=yes --with-ffmpeg=yes \
--with-ffmpeg-includes=/usr/local/include/ffmpeg

*if OpenGL fails then maybe it is necessary to link '''glxATI.h''' with '''glx.h''' and re-run the configuration

cd /usr/include/GL

sudo ln glxATI.h glx.h

* compilation
make

* compilation is expected to end with a statement similar to the following:

Started compilation: Wed Feb 27 00:24:36 CET 2008
--
Errors in:
No errors detected.

* installation
sudo checkinstall

* launch 64-bit GRASS.6.4.svn
grass64

'''Notes'''
* in case of errors in future compilation attempts, remember to remove program binaries with
make clean
* and the files created with the "configuration" from previous compilations with
make distclean

== Ubuntu 6.06, 7.10 ==

* [http://david.p.finlayson.googlepages.com/makegrass.sh makegrass.sh] is script designed to automate most of the download, configuration and compilation of GRASS 6.x-CVS
** it is advised use [https://help.ubuntu.com/community/CheckInstall checkinstall] (''sudo apt-get install checkinstall'') instead of ''make install'' to keep track of installed software
** Think twice before using this script. Some users experienced problems such as disabled XGL etc.
* [[User:Steko/Automated_CVS_compiling|Here]] is another of these scripts, it's homemade so probably you'll find the above more useful for production sites.

[[Category: Compilation]]
[[Category: Installation]]
[[Category: Ubuntu]]
[[Category: FAQ]]

File:Comparison Paris.png

2016-09-28T09:28:33Z

⚠️Marco.minghini: Marco.minghini uploaded a new version of File:Comparison Paris.png

Maximum deviation of the Paris OpenStreetMap dataset from the IGN dataset on a square grid with 1km x 1km cell size.

ISPRS XXIII Congress 2016: GRASS related workshops and presentations

2016-09-28T09:27:28Z

⚠️Marco.minghini: /* An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network */

== Introduction ==

ISPRS XXIII Congress 2016 (International Society for Photogrammetry and Remote Sensing), Prague, Czech Republic, July 12-19, 2016, http://www.isprs2016-prague.com/

== Presentations ==

=== Overland flow analysis using time series of sUAS-derived elevation models ===

[[File:Jeziorska uav grass puddle mar 18.png|300px|thumb|right|Comparison of simulated surface water flow and puddle in orthophoto (''Overland flow analysis using time series of sUAS-derived elevation models'')]]

* Justyna Jeziorska, Helena Mitasova, Anna Petrasova, Vaclav Petras, Darshan Divakaran, Thomas Zajkowski (2016): ''Overland flow analysis using time series of sUAS-derived elevation models'', ISPRS
** Short Abstract: We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data.
** Abstract: With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
** Keywords: UAS, UAV, sUAS, lidar, digital elevation model, overland flow modeling, path sampling
** Part of ThS 2 - Operational Remote Sensing Application Services
** Full Paper for ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals): [http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-8/159/2016/ ISPRS Annals], [https://www.researchgate.net/publication/303845667_OVERLAND_FLOW_ANALYSIS_USING_TIME_SERIES_OF_SUAS-DERIVED_ELEVATION_MODELS ResearchGate]
** Related modules: {{cmd|v.in.lidar}}, {{cmd|v.surf.rst}}, {{cmd|r.sim.water}}

=== Tangible Landscape: Cognitively grasping the flow of water ===

[[File:Tangible landscape termite game.jpg|300px|thumb|right|Playing termites game during Coffee and Viz in NC State Hunt library (''Spatial cognition in tangible computing'')]]

* Brendan Alexander Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, Ross K. Meentemeyer (2016): ''Spatial cognition in tangible computing'', ISPRS
** Short Abstract: We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), that physically manifests data so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. We ran a terrain modeling experiment with 39 participants and found that tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies.
** Abstract: Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging requiring training and highly abstract thinking that adds a new cognitive burden. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through an continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a terrain modeling experiment with 39 participants to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used topographic and morphometric parameters, differencing, hydrological simulation, and spatial statistics to quantitatively assess spatial performance. We found that Tangible Landscape generally enhanced 3D spatial performance, but future work is need to understand the role of cognition, affect, motivation, and metacognition in tangible computing.
** Keywords: Embodied cognition, spatial thinking, spatial performance, tangible user interfaces, user experiment, 3D
** Part of ThS 16 - Perceptual and cognitive experiments with imagery and 3D models
** Website: http://tangible-landscape.github.io/
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/647/2016/ ISPRS Archives], [https://www.researchgate.net/publication/303870744_Tangible_Landscape_cognitively_grasping_the_flow_of_water ResearchGate]

=== Open source approach to urban growth simulation ===

[[File:Futures grass gis asheville.png|300px|thumb|right|Urban growth projection (''Open source approach to urban growth simulation'')]]

* Anna Petrasova, Vaclav Petras, Derek Van Berkel, Brendan A. Harmon, Helena Mitasova, Ross K. Meentemeyer (2016): ''Open source approach to urban growth simulation'', ISPRS
** Short Abstract: Urban growth scenario simulation is a powerful tool for exploring impacts of urbanization on the landscape and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) - a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We apply FUTURES to explore trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Abstract: Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre and open source GIS and research platform for geospatial domain. GRASS GIS provides efficient libraries for FUTURES model development as well as standard GIS tools and graphical user interface for model users. To support adoption of FUTURES, we developed a tutorial and a dataset for North Carolina, compatible with the official GRASS GIS sample dataset. Both tutorial and documentation leverage the existing GRASS GIS infrastructure. Releasing FUTURES as a GRASS GIS addon simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future. By providing this simple-to-use model with documentation and the sample dataset, we enable researchers to experiment with the model, explore its potential or even modify the model for their applications. Open source FUTURES was applied in different contexts, including coupling with ecosystem services in mountainous parts of North Carolina, projection of urban spread in South Atlantic USA megaregion and most recently exploring the trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Keywords: GRASS GIS, FUTURES, urbanization, open science, simulation
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{AddonCmd|r.futures}}, {{AddonCmd|r.futures.pga}}, ...
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/953/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340421_Open_source_approach_to_urban_growth_simulation ResearchGate]

=== Processing UAV and lidar point clouds in GRASS GIS ===

[[File:Range on ground from north.png|300px|thumb|right|Range of z coordinates displayed on ground (''Processing UAV and lidar point clouds in GRASS GIS'')]]

* Vaclav Petras, Anna Petrasova, Justyna Jeziorska, Helena Mitasova (2016): ''Processing UAV and lidar point clouds in GRASS GIS'', ISPRS
** Short Abstract: Current methods of acquiring Earth surface data, namely lidar and UAV imagery, are generating large point clouds which vary in their properties such as density or quality. We present a set of tools with applications including but not limited to using lidar data and 3D rasters to support vegetation classification, obtaining digital surface model from UAV data, and measuring small physical models using low-cost 3D scanner. The tools are open source and implemented in a well-established open source project GRASS GIS.
** Abstract: Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, are non-selectively collecting or generating large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, developing custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
** Keywords: 3D rasters, decimation, LAS, PDAL, PCL, Kinect
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{cmd|r.in.lidar}}, {{cmd|v.in.lidar}}, {{cmd|r3.in.lidar}}, {{cmd|v.surf.rst}}
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/945/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340172_Processing_UAV_and_lidar_point_clouds_in_GRASS_GIS ResearchGate]

=== An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network ===

[[File:Comparison_Paris.png|300px|thumb|right|Maximum deviation of the Paris OpenStreetMap dataset from the IGN dataset on a square grid with 1km x 1km cell size]]

* Maria Antonia Brovelli, Marco Minghini, Monia Elisa Molinari (2016): ''An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network'', ISPRS
** Short Abstract: OpenStreetMap (OSM) is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? We present a set of GRASS GIS modules which allow users : i) a preliminary comparison between OSM and authoritative datasets, ii) a geometric preprocessing of OSM dataset and iii) the evaluation of OSM spatial accuracy using a grid-based approach. We propose also the results of the application of this set of modules to the case study of Paris road network.
** Abstract: OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.
** Keywords: Accuracy, FOSS4G, GRASS, Open data, OpenStreetMap, Road network, Volunteered Geographic Information
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.precomp v.osm.precomp], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.preproc v.osm.preproc], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.acc v.osm.acc]
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/919/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340227_An_automated_GRASS-based_procedure_to_assess_the_geometrical_accuracy_of_the_OpenStreetMap_Paris_road_network ResearchGate]

== Meetup ==

See [[GRASS GIS ISPRS Prague meetup 2016]].

[[Category: Conferences]]
[[Category: 2016]]

File:Comparison Paris.png

2016-09-28T09:19:24Z

⚠️Marco.minghini: Maximum deviation of the Paris OpenStreetMap dataset from the IGN dataset on a square grid with 1km x 1km cell size.

Maximum deviation of the Paris OpenStreetMap dataset from the IGN dataset on a square grid with 1km x 1km cell size.

ISPRS XXIII Congress 2016: GRASS related workshops and presentations

2016-08-16T12:05:47Z

⚠️Marco.minghini: /* An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network */

== Introduction ==

ISPRS XXIII Congress 2016 (International Society for Photogrammetry and Remote Sensing), Prague, Czech Republic, July 12-19, 2016, http://www.isprs2016-prague.com/

== Presentations ==

=== Overland flow analysis using time series of sUAS-derived elevation models ===

[[File:Jeziorska uav grass puddle mar 18.png|300px|thumb|right|Comparison of simulated surface water flow and puddle in orthophoto (''Overland flow analysis using time series of sUAS-derived elevation models'')]]

* Justyna Jeziorska, Helena Mitasova, Anna Petrasova, Vaclav Petras, Darshan Divakaran, Thomas Zajkowski (2016): ''Overland flow analysis using time series of sUAS-derived elevation models'', ISPRS
** Short Abstract: We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data.
** Abstract: With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
** Keywords: UAS, UAV, sUAS, lidar, digital elevation model, overland flow modeling, path sampling
** Part of ThS 2 - Operational Remote Sensing Application Services
** Full Paper for ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals): [http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-8/159/2016/ ISPRS Annals], [https://www.researchgate.net/publication/303845667_OVERLAND_FLOW_ANALYSIS_USING_TIME_SERIES_OF_SUAS-DERIVED_ELEVATION_MODELS ResearchGate]
** Related modules: {{cmd|v.in.lidar}}, {{cmd|v.surf.rst}}, {{cmd|r.sim.water}}

=== Spatial cognition in tangible computing ===

[[File:Tangible landscape termite game.jpg|300px|thumb|right|Playing termites game during Coffee and Viz in NC State Hunt library (''Spatial cognition in tangible computing'')]]

* Brendan Alexander Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, Ross K. Meentemeyer (2016): ''Spatial cognition in tangible computing'', ISPRS
** Short Abstract: We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), that physically manifests data so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. We ran a terrain modeling experiment with 39 participants and found that tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies.
** Abstract: Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging requiring training and highly abstract thinking that adds a new cognitive burden. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through an continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a terrain modeling experiment with 39 participants to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used topographic and morphometric parameters, differencing, hydrological simulation, and spatial statistics to quantitatively assess spatial performance. We found that Tangible Landscape generally enhanced 3D spatial performance, but future work is need to understand the role of cognition, affect, motivation, and metacognition in tangible computing.
** Keywords: Embodied cognition, spatial thinking, spatial performance, tangible user interfaces, user experiment, 3D
** Part of ThS 16 - Perceptual and cognitive experiments with imagery and 3D models
** Website: http://tangible-landscape.github.io/
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/647/2016/ ISPRS Archives], [https://www.researchgate.net/publication/303870744_Tangible_Landscape_cognitively_grasping_the_flow_of_water ResearchGate]

=== Open source approach to urban growth simulation ===

[[File:Futures grass gis asheville.png|300px|thumb|right|Urban growth projection (''Open source approach to urban growth simulation'')]]

* Anna Petrasova, Vaclav Petras, Derek Van Berkel, Brendan A. Harmon, Helena Mitasova, Ross K. Meentemeyer (2016): ''Open source approach to urban growth simulation'', ISPRS
** Short Abstract: Urban growth scenario simulation is a powerful tool for exploring impacts of urbanization on the landscape and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) - a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We apply FUTURES to explore trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Abstract: Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre and open source GIS and research platform for geospatial domain. GRASS GIS provides efficient libraries for FUTURES model development as well as standard GIS tools and graphical user interface for model users. To support adoption of FUTURES, we developed a tutorial and a dataset for North Carolina, compatible with the official GRASS GIS sample dataset. Both tutorial and documentation leverage the existing GRASS GIS infrastructure. Releasing FUTURES as a GRASS GIS addon simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future. By providing this simple-to-use model with documentation and the sample dataset, we enable researchers to experiment with the model, explore its potential or even modify the model for their applications. Open source FUTURES was applied in different contexts, including coupling with ecosystem services in mountainous parts of North Carolina, projection of urban spread in South Atlantic USA megaregion and most recently exploring the trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Keywords: GRASS GIS, FUTURES, urbanization, open science, simulation
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{AddonCmd|r.futures}}, {{AddonCmd|r.futures.pga}}, ...
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/953/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340421_Open_source_approach_to_urban_growth_simulation ResearchGate]

=== Processing UAV and lidar point clouds in GRASS GIS ===

[[File:Range on ground from north.png|300px|thumb|right|Range of z coordinates displayed on ground (''Processing UAV and lidar point clouds in GRASS GIS'')]]

* Vaclav Petras, Anna Petrasova, Justyna Jeziorska, Helena Mitasova (2016): ''Processing UAV and lidar point clouds in GRASS GIS'', ISPRS
** Short Abstract: Current methods of acquiring Earth surface data, namely lidar and UAV imagery, are generating large point clouds which vary in their properties such as density or quality. We present a set of tools with applications including but not limited to using lidar data and 3D rasters to support vegetation classification, obtaining digital surface model from UAV data, and measuring small physical models using low-cost 3D scanner. The tools are open source and implemented in a well-established open source project GRASS GIS.
** Abstract: Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, are non-selectively collecting or generating large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, developing custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
** Keywords: 3D rasters, decimation, LAS, PDAL, PCL, Kinect
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{cmd|r.in.lidar}}, {{cmd|v.in.lidar}}, {{cmd|r3.in.lidar}}, {{cmd|v.surf.rst}}
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/945/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340172_Processing_UAV_and_lidar_point_clouds_in_GRASS_GIS ResearchGate]

=== An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network ===

* Maria Antonia Brovelli, Marco Minghini, Monia Elisa Molinari (2016): ''An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network'', ISPRS
** Short Abstract: OpenStreetMap (OSM) is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? We present a set of GRASS GIS modules which allow users : i) a preliminary comparison between OSM and authoritative datasets, ii) a geometric preprocessing of OSM dataset and iii) the evaluation of OSM spatial accuracy using a grid-based approach. We propose also the results of the application of this set of modules to the case study of Paris road network.
** Abstract: OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.
** Keywords: Accuracy, FOSS4G, GRASS, Open data, OpenStreetMap, Road network, Volunteered Geographic Information
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.precomp v.osm.precomp], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.preproc v.osm.preproc], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.acc v.osm.acc]
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/919/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340227_An_automated_GRASS-based_procedure_to_assess_the_geometrical_accuracy_of_the_OpenStreetMap_Paris_road_network ResearchGate]

== Meetup ==

See [[GRASS GIS ISPRS Prague meetup 2016]].

[[Category: Conferences]]
[[Category: 2016]]

ISPRS XXIII Congress 2016: GRASS related workshops and presentations

2016-08-16T12:04:57Z

⚠️Marco.minghini:

== Introduction ==

ISPRS XXIII Congress 2016 (International Society for Photogrammetry and Remote Sensing), Prague, Czech Republic, July 12-19, 2016, http://www.isprs2016-prague.com/

== Presentations ==

=== Overland flow analysis using time series of sUAS-derived elevation models ===

[[File:Jeziorska uav grass puddle mar 18.png|300px|thumb|right|Comparison of simulated surface water flow and puddle in orthophoto (''Overland flow analysis using time series of sUAS-derived elevation models'')]]

* Justyna Jeziorska, Helena Mitasova, Anna Petrasova, Vaclav Petras, Darshan Divakaran, Thomas Zajkowski (2016): ''Overland flow analysis using time series of sUAS-derived elevation models'', ISPRS
** Short Abstract: We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data.
** Abstract: With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
** Keywords: UAS, UAV, sUAS, lidar, digital elevation model, overland flow modeling, path sampling
** Part of ThS 2 - Operational Remote Sensing Application Services
** Full Paper for ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals): [http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-8/159/2016/ ISPRS Annals], [https://www.researchgate.net/publication/303845667_OVERLAND_FLOW_ANALYSIS_USING_TIME_SERIES_OF_SUAS-DERIVED_ELEVATION_MODELS ResearchGate]
** Related modules: {{cmd|v.in.lidar}}, {{cmd|v.surf.rst}}, {{cmd|r.sim.water}}

=== Spatial cognition in tangible computing ===

[[File:Tangible landscape termite game.jpg|300px|thumb|right|Playing termites game during Coffee and Viz in NC State Hunt library (''Spatial cognition in tangible computing'')]]

* Brendan Alexander Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, Ross K. Meentemeyer (2016): ''Spatial cognition in tangible computing'', ISPRS
** Short Abstract: We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), that physically manifests data so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. We ran a terrain modeling experiment with 39 participants and found that tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies.
** Abstract: Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging requiring training and highly abstract thinking that adds a new cognitive burden. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through an continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a terrain modeling experiment with 39 participants to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used topographic and morphometric parameters, differencing, hydrological simulation, and spatial statistics to quantitatively assess spatial performance. We found that Tangible Landscape generally enhanced 3D spatial performance, but future work is need to understand the role of cognition, affect, motivation, and metacognition in tangible computing.
** Keywords: Embodied cognition, spatial thinking, spatial performance, tangible user interfaces, user experiment, 3D
** Part of ThS 16 - Perceptual and cognitive experiments with imagery and 3D models
** Website: http://tangible-landscape.github.io/
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/647/2016/ ISPRS Archives], [https://www.researchgate.net/publication/303870744_Tangible_Landscape_cognitively_grasping_the_flow_of_water ResearchGate]

=== Open source approach to urban growth simulation ===

[[File:Futures grass gis asheville.png|300px|thumb|right|Urban growth projection (''Open source approach to urban growth simulation'')]]

* Anna Petrasova, Vaclav Petras, Derek Van Berkel, Brendan A. Harmon, Helena Mitasova, Ross K. Meentemeyer (2016): ''Open source approach to urban growth simulation'', ISPRS
** Short Abstract: Urban growth scenario simulation is a powerful tool for exploring impacts of urbanization on the landscape and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) - a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We apply FUTURES to explore trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Abstract: Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We present FUTURES (FUTure Urban-Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling framework as a case showing how an originally closed and inaccessible model can benefit from integration into open source GIS. We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre and open source GIS and research platform for geospatial domain. GRASS GIS provides efficient libraries for FUTURES model development as well as standard GIS tools and graphical user interface for model users. To support adoption of FUTURES, we developed a tutorial and a dataset for North Carolina, compatible with the official GRASS GIS sample dataset. Both tutorial and documentation leverage the existing GRASS GIS infrastructure. Releasing FUTURES as a GRASS GIS addon simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future. By providing this simple-to-use model with documentation and the sample dataset, we enable researchers to experiment with the model, explore its potential or even modify the model for their applications. Open source FUTURES was applied in different contexts, including coupling with ecosystem services in mountainous parts of North Carolina, projection of urban spread in South Atlantic USA megaregion and most recently exploring the trade-offs of urban growth scenarios using Tangible Landscape, a collaborative modeling platform with tangible interaction coupling a physical model with GIS.
** Keywords: GRASS GIS, FUTURES, urbanization, open science, simulation
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{AddonCmd|r.futures}}, {{AddonCmd|r.futures.pga}}, ...
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/953/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340421_Open_source_approach_to_urban_growth_simulation ResearchGate]

=== Processing UAV and lidar point clouds in GRASS GIS ===

[[File:Range on ground from north.png|300px|thumb|right|Range of z coordinates displayed on ground (''Processing UAV and lidar point clouds in GRASS GIS'')]]

* Vaclav Petras, Anna Petrasova, Justyna Jeziorska, Helena Mitasova (2016): ''Processing UAV and lidar point clouds in GRASS GIS'', ISPRS
** Short Abstract: Current methods of acquiring Earth surface data, namely lidar and UAV imagery, are generating large point clouds which vary in their properties such as density or quality. We present a set of tools with applications including but not limited to using lidar data and 3D rasters to support vegetation classification, obtaining digital surface model from UAV data, and measuring small physical models using low-cost 3D scanner. The tools are open source and implemented in a well-established open source project GRASS GIS.
** Abstract: Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, are non-selectively collecting or generating large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, developing custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
** Keywords: 3D rasters, decimation, LAS, PDAL, PCL, Kinect
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: {{cmd|r.in.lidar}}, {{cmd|v.in.lidar}}, {{cmd|r3.in.lidar}}, {{cmd|v.surf.rst}}
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/945/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340172_Processing_UAV_and_lidar_point_clouds_in_GRASS_GIS ResearchGate]

=== An Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network ===

* Maria Antonia Brovelli, Marco Minghini, Monia Elisa Molinari (2016): ''n Automated GRASS-Based Procedure to Assess the Geometrical Accuracy of the OpenStreetMap Paris Road Network'', ISPRS
** Short Abstract: OpenStreetMap (OSM) is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? We present a set of GRASS GIS modules which allow users : i) a preliminary comparison between OSM and authoritative datasets, ii) a geometric preprocessing of OSM dataset and iii) the evaluation of OSM spatial accuracy using a grid-based approach. We propose also the results of the application of this set of modules to the case study of Paris road network.
** Abstract: OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.
** Keywords: Accuracy, FOSS4G, GRASS, Open data, OpenStreetMap, Road network, Volunteered Geographic Information
** Part of Special Session: SpS 10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
** Related modules: [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.precomp v.osm.precomp], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.preproc v.osm.preproc], [https://github.com/MoniaMolinari/OSM-roads-comparison/tree/master/GRASS-scripts/v.osm.acc v.osm.acc]
** Paper: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/919/2016/ ISPRS Archives], [https://www.researchgate.net/publication/304340227_An_automated_GRASS-based_procedure_to_assess_the_geometrical_accuracy_of_the_OpenStreetMap_Paris_road_network ResearchGate]

== Meetup ==

See [[GRASS GIS ISPRS Prague meetup 2016]].

[[Category: Conferences]]
[[Category: 2016]]