GRASS GIS at FOSS4G Boston 2017: Difference between revisions

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* Description: Public webcams provide us with unique information about the dynamics of public spaces and active transportation behavior such as walking or bicycling. We compiled webcam images capturing public squares of towns and cities in several locations around the world and used crowdsourcing platform Amazon Mechanical Turk (MTurk) to locate pedestrians in these images. In this presentation we will show how we used several scientific Python packages and Jupyter Notebook to turn raw MTurk data into a georeferenced dataset suitable for further analysis and visualization.  Using space-time cube representation, we then estimated spatio-temporal density of pedestrians and interactively explored the resulting voxel model in GRASS GIS. Harnessing the power of open source software and crowdsourced data, this study opens new possibilities for the analysis of the dynamics of public spaces within their geospatial context.
* Description: Public webcams provide us with unique information about the dynamics of public spaces and active transportation behavior such as walking or bicycling. We compiled webcam images capturing public squares of towns and cities in several locations around the world and used crowdsourcing platform Amazon Mechanical Turk (MTurk) to locate pedestrians in these images. In this presentation we will show how we used several scientific Python packages and Jupyter Notebook to turn raw MTurk data into a georeferenced dataset suitable for further analysis and visualization.  Using space-time cube representation, we then estimated spatio-temporal density of pedestrians and interactively explored the resulting voxel model in GRASS GIS. Harnessing the power of open source software and crowdsourced data, this study opens new possibilities for the analysis of the dynamics of public spaces within their geospatial context.
* Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview
* Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview, Blender
 
== OSGeo booth ==
[[File:OSGeo_booth_Boston_2017.jpg|400px|thumb|right| OSGeo booth with Tangible Landscape demo]]
* From GRASS GIS community Helena Mitasova, Vaclav Petras, Anna Petrasova and Payam Tabrizian volunteered at the booth
* [http://tangible-landscape.github.io/ Tangible Landscape] demo
* GRASS GIS flyers and stickers


== Code Sprint ==
== Code Sprint ==

Revision as of 20:17, 20 August 2017

International Conference for Free and Open Source Software for Geospatial, August 14-19, 2017, Boston, Massachusetts, USA, http://2017.foss4g.org

From GRASS GIS novice to power user: GRASS GIS graphical user interface showing Vector Network Analysis Tool
Processing lidar and UAV point clouds in GRASS GIS: Range of z coordinates displayed on terrain surface

Workshops

Conference program link: http://2017.foss4g.org/workshops

From GRASS GIS novice to power user

by Anna Petrasova from North Carolina State University (NCSU), Giuseppe Amatulli from Yale University, and Vaclav Petras from NCSU

  • Description: Do you want to use GRASS GIS, but never understood what that location and mapset are? Do you struggle with the computational region? Or perhaps you used GRASS GIS already but you wonder what g.region -a does? Maybe you were never comfortable with GRASS command line? In this workshop, we will explain and practice all these functions and answer questions more advanced users may have. We will help you decide when to use graphical user interface and when to use the power of command line. We will go through simple examples of vector, raster, and image processing functionality and we will try couple of new and old tools such as vector network analysis or image segmentation which might be the reason you want to use GRASS GIS. We aim this workshop at absolute beginners without prior knowledge of GRASS GIS, but we hope it can be useful also to current users looking for deeper understanding of basic concepts or the curious ones who want to try the latest additions to GRASS GIS.
  • Outline: Introduction to the GRASS folder structure and environment, Introduction to graphical user interface and command line, GRASS working environment and working directory, Create location and mapset (from GUI and from command line), Setting the study area extent and pixel size to make the computation correct and efficient, Move data to and from GRASS database, Move data between locations and mapsets, Perform basic raster and vector operations, Try out some of the more advanced and latest features
  • Format and requirements: Participants should bring their laptops with GRASS GIS 7. Beginners are encouraged to try using the latest OSGeo-Live virtual machine. There are no special requirements. Just have your laptop with GRASS GIS or OSGeo-Live virtual machine with you.
  • Materials: From GRASS GIS novice to power user (workshop at FOSS4G Boston 2017)

Processing lidar and UAV point clouds in GRASS GIS

by Vaclav Petras, Anna Petrasova, and Helena Mitasova from North Carolina State University (NCSU)

  • Description: GRASS GIS offers, besides other things, numerous analytical tools for point clouds, terrain, and remote sensing. In this hands-on workshop we will explore the tools in GRASS GIS for processing point clouds obtained by lidar or through processing of UAV imagery. We will start with a brief and focused introduction into GRASS GIS graphical user interface (GUI) and we will continue with short introduction to GRASS GIS Python interface. Participants will then decide if they will use GUI, command line, Python, or online Jupyter Notebook for the rest of the workshop. We will explore the properties of the point cloud, interpolate surfaces, and perform advanced terrain analyses to detect landforms and artifacts. We will go through several terrain 2D and 3D visualization techniques to get more information out of the data and finish with vegetation analysis.
  • Outline: Basic introduction to graphical user interface, Basic introduction to Python interface, Decide if to use GUI, command line, Python, or online Jupyter Notebook, Binning of the point cloud, Interpolation, Terrain analysis, Visualization, profiles and statistics, Vegetation analysis, 3D visualization
  • Format and requirements: This workshop is accessible to beginners, but some basic knowledge of lidar processing or GIS will be helpful for smooth experience. Have your laptop with GRASS GIS or OSGeo-Live virtual machine with you. Online Jupyer Notebooks will be available too.
  • Slides for related talks from past events: http://wenzeslaus.github.io/grass-lidar-talks/
  • Related paper: Petras, V., Petrasova, A., Jeziorska, J., Mitasova, H.: Processing UAV and lidar point clouds in GRASS GIS, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7, 945–952, 2016 (full-text at ResearchGate)
  • Materials: Processing lidar and UAV point clouds in GRASS GIS (workshop at FOSS4G Boston 2017)

Presentations

Advanced geospatial technologies: The new powerful GRASS GIS 7.2 release: GRASS GIS graphical user interface with distance to forest edge computed using r.grow.distance

Conference program link: http://2017.foss4g.org/accepted-presentations

Advanced geospatial technologies: The new powerful GRASS GIS 7.2 release

by Vaclav Petras from North Carolina State University (NCSU), Markus Neteler from mundialis GmbH & Co. KG, Anna Petrasova from NCSU and Helena Mitasova from NCSU

  • Description: To solve challenges in the world and our backyard and to deal with massive data, we need to have a tool which works on powerful supercomputers as well as on small laptops. We need a tool which GIS novice can use through graphical user interface and a tool which data science expert can use through command line. We present GRASS GIS 7.2 , a powerful platform for geospatial data analysis, network analysis, topological operations, image processing, spatial modeling and more. The new 7.2 release comes with a series of new modules to analyze vector and raster data along with unique features such as temporal algebra functionality. The graphical user interface includes a new management tool for GRASS GIS spatial database and an integrated Python editor where Python beginners can get a template for script with GUI in one click. Furthermore, the libraries were again significantly improved for speed and efficiency, along with support for huge files. The development team added lidar data support as well as new methods for 3D flow accumulation and 3D gradient calculation. Importantly, raster data are now significantly reduced in size through new efficient raster data compression methods. For the ease of automated processing, batch job support was greatly simplified. In addition, there are more than 50 new add-ons available. The talk shows latest advances in GRASS GIS 7.2, proposing to directly enjoy the software rather than to use it in a limited way through other software.

Visualization and analysis of active transportation patterns derived from public webcams

Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams: Space time cube representation of pedestrian density in Ehingen, Germany

by Anna Petrasova, Aaron Hipp and Helena Mitasova from North Carolina State University

  • Description: Public webcams provide us with unique information about the dynamics of public spaces and active transportation behavior such as walking or bicycling. We compiled webcam images capturing public squares of towns and cities in several locations around the world and used crowdsourcing platform Amazon Mechanical Turk (MTurk) to locate pedestrians in these images. In this presentation we will show how we used several scientific Python packages and Jupyter Notebook to turn raw MTurk data into a georeferenced dataset suitable for further analysis and visualization. Using space-time cube representation, we then estimated spatio-temporal density of pedestrians and interactively explored the resulting voxel model in GRASS GIS. Harnessing the power of open source software and crowdsourced data, this study opens new possibilities for the analysis of the dynamics of public spaces within their geospatial context.
  • Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview, Blender

OSGeo booth

OSGeo booth with Tangible Landscape demo
  • From GRASS GIS community Helena Mitasova, Vaclav Petras, Anna Petrasova and Payam Tabrizian volunteered at the booth
  • Tangible Landscape demo
  • GRASS GIS flyers and stickers

Code Sprint

General information and sign up is here:

Add yourself also here if you plan to be there on August 19 and work on or work with GRASS GIS.


GRASS GIS 7 sprint team at FOSS4G 2017, Boston

See also

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