Training Course on GDAL/OGR Command-Line Tools Mastery

Training Course on GDAL/OGR Command-Line Tools Mastery

Introduction

Training Course on GDAL/OGR Command-Line Tools Mastery offers an in-depth exploration of GDAL (Geospatial Data Abstraction Library) and OGR (OpenGIS Simple Features Reference Implementation) command-line utilities. These powerful, open-source tools are indispensable for anyone working with geospatial data, enabling efficient data manipulation, format conversion, reprojection, and automation of complex geoprocessing workflows. Participants will gain the practical skills to expertly handle diverse raster and vector datasets, from satellite imagery to GIS shapefiles, ensuring data integrity and optimizing processing efficiency.

The course emphasizes hands-on learning and real-world case studies, bridging the gap between theoretical knowledge and practical application. By mastering GDAL/OGR, professionals can significantly enhance their geospatial analysis capabilities, streamline data pipelines, and achieve greater productivity in remote sensing, cartography, environmental science, and urban planning. This expertise is crucial for tackling large-scale geospatial big data challenges and building robust, automated solutions.

Course Duration

10 days

Course Objectives

1. Comprehend the fundamental data models and drivers for both raster and vector data.
2. Successfully set up and optimize the command-line environment on various operating systems.
3. Fluently convert between a multitude of geospatial formats
4. Apply commands for clipping, resampling, merging, and band manipulation of raster datasets.
5. Utilize OGR for filtering, spatial querying, attribute manipulation, and reprojection of vector layers.
6. Accurately define, inspect, and transform CRS for diverse datasets using gdalwarp and ogr2ogr.
7. Develop batch processing scripts using GDAL/OGR for repetitive tasks, improving efficiency.
8. Generate DEM derivatives like hillshades, slopes, and aspects.
9. Apply techniques for orthorectification, pan-sharpening, and radiometric correction.
10. Understand how to leverage GDAL/OGR within Python or Bash scripts for advanced automation.
11. Align unreferenced imagery to a known coordinate system.
12. Create and manage virtual raster datasets for on-the-fly processing and mosaic creation.
13. Effectively identify and resolve common issues encountered with GDAL/OGR commands.

Organizational Benefits

• Eliminate reliance on expensive proprietary GIS software licenses.
• Automate repetitive geospatial tasks, dramatically reducing processing time and manual effort.
• Seamlessly work with diverse geospatial data formats from various sources.
• Process large volumes of geospatial data efficiently, supporting big data initiatives.
• Equip teams with a versatile skillset for complex geospatial challenges.
• Standardize geospatial data processing pipelines across departments.
• Tools for data validation and transformation ensure higher data integrity.
• Develop tailored solutions and integrate with existing systems using open-source flexibility.

Target Audience

1. GIS Analysts & Specialists:
2. Remote Sensing Professionals
3. Data Scientists & Engineers
4. Environmental Scientists
5. Urban Planners & Researchers
6. Software Developers
7. Cartographers.
8. Anyone involved in managing, processing, or analyzing large volumes of geospatial data.

Course Modules

Module 1: Introduction to GDAL/OGR & Command Line Basics

• Overview: Understanding GDAL (raster) and OGR (vector) libraries.
• Installation: Setting up GDAL/OGR on Windows, Linux, and macOS.
• Command Line Fundamentals: Basic syntax, common options, and navigating the terminal.
• Help & Documentation: Effectively using gdalinfo -h and ogrinfo -h for self-help.
• Data Model: Introduction to raster and vector data models and their abstract representation in GDAL/OGR.
• Case Study: Inspecting various geospatial files (e.g., GeoTIFF, Shapefile, KML) to understand their structure and metadata using gdalinfo and ogrinfo.

Module 2: Raster Data Basics with GDAL Utilities

• gdalinfo: Extracting metadata, projection, and band information from raster files.
• gdal_translate: Converting raster formats (e.g., TIFF to PNG, JPEG to GeoTIFF).
• gdal_edit.py: Modifying raster metadata, projections, and geotransforms.
• gdaladdo: Creating overviews (pyramids) for faster display and performance.
• gdalwarp: Introduction to raster reprojection and mosaic building.
• Case Study: Converting a collection of satellite image tiles from one format to another and generating overviews for web mapping applications.

Module 3: Advanced Raster Processing: Clipping & Resampling

• gdal_translate -projwin: Clipping rasters by geographic coordinates.
• gdalwarp -cutline: Clipping rasters using a vector polygon.
• gdal_translate -outsize: Resampling rasters to a new resolution.
• Interpolation Methods: Understanding different resampling algorithms (e.g., nearest neighbor, bilinear, cubic).
• Nodata Values: Handling and managing nodata values in raster datasets.
• Case Study: Extracting a specific region of interest from a large DEM using a shapefile boundary and resampling it for a web-based terrain visualization.

Module 4: Raster Merging & Band Manipulation

• gdal_merge.py: Stitching multiple raster tiles into a single mosaic.
• gdal_calc.py: Performing raster algebra (e.g., calculating NDVI from separate bands).
• Band Selection: Extracting or reordering specific bands from a multi-band raster.
• Virtual Rasters (VRT): Creating VRTs for on-the-fly merging without physical file creation.
• Color Relief: Applying color ramps and hillshade effects for visualization.
• Case Study: Merging several Landsat scene tiles to create a seamless mosaic of a large area and then calculating NDVI to assess vegetation health.

Module 5: Working with Digital Elevation Models (DEMs)

• gdaldem: Generating hillshade, slope, aspect, and terrain roughness maps.
• Contour Generation: Creating contour lines from a DEM using gdal_contour.
• Viewshed Analysis: Introduction to basic viewshed calculations.
• Data Types: Understanding integer and float data types for DEMs.
• Source Data: Exploring common DEM sources (e.g., SRTM, ASTER, LiDAR).
• Case Study: Generating a hillshade map and slope map from a downloaded SRTM DEM to analyze terrain characteristics for a proposed construction site.

Module 6: Vector Data Basics with OGR Utilities

• ogrinfo: Inspecting vector layer metadata, fields, and spatial reference.
• ogr2ogr: Converting vector formats (e.g., Shapefile to GeoJSON, KML to GPKG).
• SQL Queries: Filtering vector features using OGR's SQL dialect.
• Attribute Manipulation: Adding, deleting, or modifying fields.
• Creating New Layers: Defining new vector layers and adding features.
• Case Study: Converting a large OpenStreetMap dataset (OSM PBF) to a GeoPackage and filtering out only specific feature types like roads or buildings using SQL.

Module 7: Vector Data Reprojection & Transformation

• ogr2ogr -t_srs: Reprojecting vector data between different Coordinate Reference Systems.
• -s_srs: Specifying the source CRS explicitly.
• Coordinate Transformation: Understanding the implications of different transformations.
• Datum Shifts: Awareness of datum transformations and their importance.
• Proj Strings & EPSG Codes: Working with various CRS definitions.
• Case Study: Reprojecting a national administrative boundary shapefile from a geographic CRS (e.g., WGS84) to a local projected CRS for accurate area calculations.

Module 8: Vector Data Manipulation & Geoprocessing

• ogr2ogr -clipsrc: Clipping vector layers using a bounding box or another vector layer.
• ogr2ogr -sql: Advanced spatial queries and attribute joins.
• -overwrite & -append: Managing output file behavior.
• -segmentize: Densifying lines and polygons.
• Dissolving Features: Merging adjacent features based on common attributes.
• Case Study: Clipping a road network to a specific municipal boundary and then performing an attribute query to select only primary roads.

Module 9: Geospatial Data Automation with Shell Scripting

• Bash Scripting Basics: Introduction to creating and executing simple shell scripts.
• Looping & Iteration: Processing multiple files automatically using for loops.
• Conditional Statements: Implementing logic for dynamic processing.
• Input/Output Redirection: Managing command output and input from files.
• Error Handling: Basic strategies for managing script errors.
• Case Study: Developing a batch script to reproject and convert an entire directory of raster images from different sources to a unified CRS and format.

Module 10: Advanced GDAL Features & Workflows

• gdal_rasterize: Burning vector features into a raster.
• gdal_sieve.py: Removing small, isolated pixel regions from a raster.
• gdal_proximity.py: Calculating proximity to features in a raster.
• gdal_polygonize.py: Converting raster polygons to vector polygons.
• Dataset Options: Exploring driver-specific creation and open options.
• Case Study: Creating a raster impact zone from a point dataset (e.g., pollution sources) and then vectorizing the resulting buffer for further analysis.

Module 11: Georeferencing with GDAL

• Ground Control Points (GCPs): Understanding their role in georeferencing.
• gdal_translate -gcp: Adding GCPs to an unreferenced image.
• gdalwarp for Rectification: Using gdalwarp to transform the image based on GCPs.
• Transformation Algorithms: Polynomial and Thin Plate Spline transformations.
• Image Pre-processing: Preparing images for accurate georeferencing.
• Case Study: Georeferencing a scanned historical map to a modern coordinate system to allow for overlay with current GIS data.

Module 12: Working with Web Services (WMS, WFS)

• WMS (Web Map Service): Accessing and downloading raster data from WMS servers using GDAL.
• WFS (Web Feature Service): Querying and downloading vector data from WFS servers using OGR.
• Service Capabilities: Inspecting the capabilities of OGC web services.
• Filtering Web Data: Applying spatial and attribute filters to web service requests.
• Performance Considerations: Optimizing requests for large datasets.
• Case Study: Downloading current weather radar imagery from a WMS server and overlaying it with local road networks from a WFS for real-time analysis.

Module 13: Interfacing GDAL/OGR with Python

• osgeo Library: Introduction to the Python bindings for GDAL/OGR.
• Reading Data: Accessing raster and vector datasets programmatically.
• Writing Data: Creating new geospatial files with Python.
• Raster & Vector API: Performing operations like reading pixel values, querying features, and creating geometries.
• Scripting Complex Workflows: Building more sophisticated and dynamic geospatial applications.
• Case Study: Developing a Python script to automate the daily download, processing (reprojection, clipping), and saving of satellite weather data.

Module 14: Advanced Data Formats & Drivers

• GeoPackage (GPKG): Understanding this modern, open, and efficient SQLite-based format.
• NetCDF & HDF5: Working with scientific data formats.
• JSON-based Formats: GeoJSON and TopoJSON.
• LiDAR Data (LAS/LAZ): Basic handling of point cloud data.
• Virtual Formats: Leveraging VRT for complex data structures and on-the-fly transformations.
• Case Study: Processing a large LiDAR point cloud dataset to generate a bare-earth DEM and then converting it to a standard GeoTIFF format for further use.

Module 15: Performance Optimization & Best Practices

• Command-Line Options: Utilizing flags like -co (creation options) and -ovr (overviews) for performance.
• Block Processing: Understanding how GDAL handles large datasets in blocks.
• Memory Management: Strategies for efficient processing of large files.
• Hardware Considerations: Impact of CPU, RAM, and SSDs on performance.
• Troubleshooting Common Issues: Debugging projection errors, format inconsistencies, and data corruption.
• Case Study: Optimizing a batch processing script that converts large, uncompressed imagery to a tiled, compressed GeoTIFF format with overviews for faster web display.

Training Methodology

• Hands-on Labs.
• Instructor-Led Demonstrations.
• Real-World Case Studies.
• Interactive Q&A Sessions.
• Problem-Solving Approach.
• Reference Materials.
• Post-Training Support.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.