Education is aimed to acquire knowledge and it’s based on the learning process and as any process has some steps and means that have to be analyzed. Traditionally, if you want to get knowledge you can go to a university, get a degree, visit a library, but why don’t you just check a Youtube channel; will you get the same knowledge? Will you do better research with what you learned on video? Those are excellent questions to pose in these times where we need knowledge that shapes our future in the context of climate change.
QGIS and open source GIS tools are a topic of many Youtube videos, and somehow, some channels became more relevant when people look for answers on its common problems with the use of these tools. We wanted to give a list of the most relevant Youtube channels on QGIS based on a previous selection by Hans van der Kwast.
The list has no specific order since the topics of the channels might differ among different applications of the GIS tools however a filter has been performed on channels with more than 5000 subscriptors. The amount of subscriptor is of Nov 3, 2021.
Hans van der Kwast
Description: An excellent resource to know about QGIS for water resources. Videos are well edited and Hans has a method and the patience to explain from the basic concepts to real applications.
Well edited videos about specific topics of QGIS and its integration with other softwares.
This is the channel that you have to follow if you are working witn land cover classification and more.
Tips, tricks, tutorials and vlogs about all things GIS.
Open Source Options
Using open source programming resources for GIS and data science. A large focus is placed on the QGIS software, including QGIS Python (PyQGIS) scripts, plugins and development. Data science and data analysis are also common topics.
Channel dedicated to GIS and geostatistics.
Our channel, dedicated to both spatial analysis with QGIS and Python and groundwater modeling.
Channel dedicated to Geographic Information Science (GIS), remote sensing, and environmental modeling; more specifically, in applying geospatial big data, machine learning, and cloud computing (e.g., Google Earth Engine) to study environmental change, especially surface water and wetland inundation dynamics.