If you have a set of drone imagery and you want to know the location and direction of the camera, this tutorial might be interesting for you. We have done an applied example that retrieves the geospatial metadata from the drone camera for a group of images and makes a geostapatial repretacion on a map with the image name available as popup. The tutorial is done under a Jupyter notebook with Python and Folium.

Open lithology datasets are scarce and most times they come in certain exchange formats that can´t be easily coupled with other softwares. Python has the tools to read and extract data from those files and provide them in more friendly formats as csv or xlsx. With the use of spatial libraries as Fiona or 3D visualization libraries as Pyvistas we can go one step further and process our data as shapefiles or vtks.

Storage, management and analysis of geospatial vector data as an ESRI shapefile is a common procedure of GIS and related professionals. The generation of these spatial files can be done not only on a desktop software but also by Python commands. We have created an applied example that shows the procedure in Python to create point, line, and polygon shapefiles from a csv file by the use of the Fiona library.

Python is a great tool for spatial analysis and geomachine learning, however sometimes the Windows operating system presents some difficulties to install and run the bunch of Python libraries such as Gdal, Fiona, Geopandas, Rasterio. We are aware that most geoscientists, water resources specialists and related professionals work on Windows, therefore we are always in the search of new ways to get Python working with all its geospatial capabilities in every computer. We have created a tutorial that shows the installation process of the Python geospatial libraries in Windows by the use of a Conda environment; the process is simple on its steps, however the sequence and factors related to the package compatibility are important on the installation.

We did a simple procedure in QGIS but unknown to us that extracts the centroids of lines and polygons with Field Calculator commands inside the attribute table. The procedure is straightforward and does not involve the use of any intermediate layer.

There are three essential things for the representation of geological data on QGIS: the spatial geological information, an appropriate symbology and the software knowledge. Once these three things are available, the potential of QGIS to represent geological maps is unlimited.

We have done a tutorial for the representation of synclines, anticlines, overturned synclines and overturned anticlines on the regional scale with QGIS.

In our perspective, 3D visualization of geospatial data has been a long desired feature that has been covered in some features from SAGA GIS or in some plugins from QGIS. This time we developed a Python script that converts point / line / polygon ESRI shapefiles (or any vector file) to unstructured grid Vtk format type (Vtu) by the use of the Python libraries Geopandas and Pyvista. The tutorial has files, scripts, and videos that show the whole procedure with some remarks on the software and spatial files and a discussion about the nature of the spatial files that presents some challenges in the data conversion.