Jan 17, 2018

GRASS GIS as described by a Google Code-In student


The Google Code-In contest is almost over. Today, January 17th, was the last day in which students could submit their work for revision. Last night we got one of the last tasks submissions for GRASS GIS within the contest. We had asked the students to write a short blog entry about GRASS GIS. Surprisingly, we got a bit more than that! Check this very nice text by Taylor Fang. Thanks tfang! And welcome!

GRASS GIS, the Geographic Resources Analysis Support System, Geographic Information System, commonly referred to as GRASS, is a free and open source software suite. It’s a founding member of the Open Source Geospatial Foundation, known as OSGeo. GRASS has been under continuous development since 1982, and can be used for geospatial data management, analysis, processing, graphics, modeling, and visualization. It is used in academic, commercial, government, and environmental settings around the world.

GRASS runs on several operating systems, including OS X and Windows. Users can interface with the software modules and other capabilities through a graphical user interface (GUI), provided by the software. The GRASS distribution includes many core modules, with add-ons created by users offered on its website.

As part of Google Code-In 2017-2018, I worked with GRASS and OSGeo mentors to complete several “tasks”. After downloading the North Carolina dataset and the GRASS program, I created a vector map and entered my name on the “console” tab of the program. This is shown below, with the map display on the right and the layer manager on the left (with the tabs at the bottom for layers, console, modules, data and Python).

After exploring some of the features of GRASS, I designed a splash screen for the GUI start-up, using the GRASS logo on the GRASS wiki and open-source fonts. This is shown below:

Through these processes, I learned about some of the map-making capabilities that GRASS offers, including the ability to choose the map display and format. These include vector network analysis, and point cloud data. Point cloud data is a representation of 3D surfaces developed from laser scanning, also known as Light Detection and Ranging (LiDAR). LiDAR is often used in self-driving car sensors, according to MIT Technology Review. GRASS GIS supports basic and advanced lidar data processing and analysis. These are some of the many aspects and capabilities of GRASS, which is continually being updated and improved.

Home >> Credits | Last change: 06-Apr-2015