Mastering GDAL: Automating Geospatial Data Processing

Learn GDAL from Installation to Automation with Python – Includes Projects like Building Count and Snow Fraction Mapping

Learn to install and use GDAL with QGIS and Anaconda to automate geospatial workflows and enable multithreaded processing for large-scale analysis. Work with real-world datasets including OpenStreetMap and Google Earth Engine (GEE), integrating automated scripts for efficient data handling. Perform raster calculations (e.g., snow fraction, building count) using gdal_calc and Python-based processing. Process raster data through reprojection, mosaicing, rasterization, and export to optimized formats like Cloud-Optimized GeoTIFF (COG) and NetCDF. Build two hands-on projects: Building count estimation and snow fraction mapping in Switzerland using real satellite data.

This course is designed for beginners and professionals alike who want to gain hands-on experience with geospatial data processing using open-source tools. You will learn how to read and interpret geospatial metadata, manipulate raster and vector data, and automate complex workflows using Python scripts and Jupyter Notebooks. All tools used in the course—QGIS, GDAL, and Anaconda—are open-source and freely available, making this course accessible to everyone. Whether you are working in climate research, urban planning, or environmental analysis, the skills learned in this course will empower you to streamline your geospatial data tasks and build scalable geospatial applications from scratch. No prior programming experience is required. This will change the way you work.