Mastering Gdal: Automating Geospatial Data Processing

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

What you'll learn

Understanding the Open Source dataset

Use GDAL tools like gdalinfo, gdalwarp, and gdal_calc for spatial data conversion and analysis.

Understand GDAL’s role in geospatial data processing and large-scale data handling.

Automate geospatial workflows with parallel processing.

Implement parallel and multi-threaded processing for handling large raster and vector datasets efficiently.

Requirements

Basic understanding of geospatial concepts like raster and vector data is helpful but not mandatory.

Description

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.

Overview

Section 1: Introduction

Lecture 1 Introduction to GDAL The Backbone of Geospatial Data Processing

Lecture 2 Introduction to Geospatial Dataset

Section 2: Installation of QGIS and Anaconda

Lecture 3 Install open software QGIS

Lecture 4 Install Python Anaconda navigator

Section 3: Everything About Open Dataset

Lecture 5 Everything you need to know about OpenStreetMap data

Lecture 6 Basics of Google Earth Engine

Section 4: Installing GDAL and Verifying the Installation

Lecture 7 Installing GDAL and verifying

Section 5: Understanding Metadata in Geospatial Data in GDAL

Lecture 8 Understanding Metadata in Geospatial Data in GDAL

Section 6: Vectorization and Rasterization using GDAL

Lecture 9 Vectorization and Rasterization using GDAL

Section 7: Raster Reprojection with GDAL: Multi-threading and Automation in Python

Lecture 10 Reprojection using GDAL

Section 8: Mosaicing using GDAL

Lecture 11 Mosaicing Raster Datasets with GDAL and Converting to NetCDF Format

Section 9: Projects

Lecture 12 Building Count dataset and cloud optimize tiff file using GDAL

Lecture 13 Snow Fraction Mapping in Switzerland Using GDAL

This course is ideal for geospatial professionals, GIS students, data scientists, geospatial developer, and remote sensing analysts who want to automate spatial data workflows using GDAL and Python. It is also valuable for anyone working with large geospatial datasets who wants to leverage multithreading and parallel computing for efficient processing.