GDAL — The Complete Guide: Converting, Reprojecting and Processing Geodata From the Command Line
GDAL is the foundation library of the GIS world, together with command-line tools for working with raster and vector data. It is open source (MIT licence), and most GIS programs — including QGIS — use it behind the scenes. Working with it directly is done on the command line or from Python.
GDAL
OSGeo
Command-line tools and a library to convert, reproject, clip and process geodata — DEMs, GeoTIFF, Shapefile and more.
Get it from the vendorWhat it is used for
- Format conversion — between dozens of raster and vector formats.
- Reprojection — moving between coordinate systems.
- Clipping and merging — elevation models and aerial imagery.
- Slopes and contours — derived from an elevation model.
- Batch processing — one script over hundreds of files.
Why it is worth learning
An engineer who receives a folder of elevation tiles in the wrong coordinate system can merge, reproject and clip them with a few commands, without opening each file separately. The same operation in a script repeats exactly on every project. If you prefer a graphical interface, most of these tools are available in the QGIS menus.
Related tools
For a full GIS program with a graphical interface — QGIS. For processing point clouds in a similar way — PDAL. For hydrological analysis of elevation models — WhiteboxTools.
QGIS
QGIS.org
Open-source GIS for spatial analysis, layer work and map production.
Get it from the vendor
PDAL
PDAL contributors (Hobu)
Point cloud processing pipelines for LiDAR — filter, classify, crop and convert LAS/LAZ, like GDAL for point clouds.
Get it from the vendor
WhiteboxTools
Whitebox Geospatial
Hundreds of geospatial analysis tools, especially strong on terrain and LiDAR point clouds.
Get it from the vendor
GRASS GIS
OSGeo
GIS with strong raster and terrain analysis — catchments, slope, visibility and elevation models.
Get it from the vendorThe bottom line
GDAL is the quiet tool that holds up the GIS world. Anyone who works a lot with geographic data will save hours of manual work by learning a few of its commands.